A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance Structure Models to Block-Toeplitz Matrices Representing Single-Subject Multivariate Time-Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

1998-01-01

Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square…

Exploiting the Modified Colombo-Nyquist Rule for Co-estimating Sub-monthly Gravity Field Solutions from a GRACE-like Mission

NASA Astrophysics Data System (ADS)

Devaraju, B.; Weigelt, M.; Mueller, J.

2017-12-01

In order to suppress the impact of aliasing errors on the standard monthly GRACE gravity-field solutions, co-estimating sub-monthly (daily/two-day) low-degree solutions has been suggested as a solution. The maximum degree of the low-degree solutions is chosen via the Colombo-Nyquist rule of thumb. However, it is now established that the sampling of satellites puts a restriction on the maximum estimable order and not the degree - modified Colombo-Nyquist rule. Therefore, in this contribution, we co-estimate low-order sub-monthly solutions, and compare and contrast them with the low-degree sub-monthly solutions. We also investigate their efficacies in dealing with aliasing errors.

Determination of stability and control parameters of a light airplane from flight data using two estimation methods. [equation error and maximum likelihood methods

NASA Technical Reports Server (NTRS)

Klein, V.

1979-01-01

Two identification methods, the equation error method and the output error method, are used to estimate stability and control parameter values from flight data for a low-wing, single-engine, general aviation airplane. The estimated parameters from both methods are in very good agreement primarily because of sufficient accuracy of measured data. The estimated static parameters also agree with the results from steady flights. The effect of power different input forms are demonstrated. Examination of all results available gives the best values of estimated parameters and specifies their accuracies.

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based estimation applications.

Cardiac conduction velocity estimation from sequential mapping assuming known Gaussian distribution for activation time estimation error.

PubMed

Shariat, Mohammad Hassan; Gazor, Saeed; Redfearn, Damian

2016-08-01

In this paper, we study the problem of the cardiac conduction velocity (CCV) estimation for the sequential intracardiac mapping. We assume that the intracardiac electrograms of several cardiac sites are sequentially recorded, their activation times (ATs) are extracted, and the corresponding wavefronts are specified. The locations of the mapping catheter's electrodes and the ATs of the wavefronts are used here for the CCV estimation. We assume that the extracted ATs include some estimation errors, which we model with zero-mean white Gaussian noise values with known variances. Assuming stable planar wavefront propagation, we derive the maximum likelihood CCV estimator, when the synchronization times between various recording sites are unknown. We analytically evaluate the performance of the CCV estimator and provide its mean square estimation error. Our simulation results confirm the accuracy of the proposed method and the error analysis of the proposed CCV estimator.

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis

PubMed Central

van de Schoot, Rens; Hox, Joop

2014-01-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions. PMID:29795827

Application of parameter estimation to highly unstable aircraft

NASA Technical Reports Server (NTRS)

Maine, R. E.; Murray, J. E.

1986-01-01

This paper discusses the application of parameter estimation to highly unstable aircraft. It includes a discussion of the problems in applying the output error method to such aircraft and demonstrates that the filter error method eliminates these problems. The paper shows that the maximum likelihood estimator with no process noise does not reduce to the output error method when the system is unstable. It also proposes and demonstrates an ad hoc method that is similar in form to the filter error method, but applicable to nonlinear problems. Flight data from the X-29 forward-swept-wing demonstrator is used to illustrate the problems and methods discussed.

Application of parameter estimation to highly unstable aircraft

NASA Technical Reports Server (NTRS)

Maine, R. E.; Murray, J. E.

1986-01-01

The application of parameter estimation to highly unstable aircraft is discussed. Included are a discussion of the problems in applying the output error method to such aircraft and demonstrates that the filter error method eliminates these problems. The paper shows that the maximum likelihood estimator with no process noise does not reduce to the output error method when the system is unstable. It also proposes and demonstrates an ad hoc method that is similar in form to the filter error method, but applicable to nonlinear problems. Flight data from the X-29 forward-swept-wing demonstrator is used to illustrate the problems and methods discussed.

The epoch state navigation filter. [for maximum likelihood estimates of position and velocity vectors

NASA Technical Reports Server (NTRS)

Battin, R. H.; Croopnick, S. R.; Edwards, J. A.

1977-01-01

The formulation of a recursive maximum likelihood navigation system employing reference position and velocity vectors as state variables is presented. Convenient forms of the required variational equations of motion are developed together with an explicit form of the associated state transition matrix needed to refer measurement data from the measurement time to the epoch time. Computational advantages accrue from this design in that the usual forward extrapolation of the covariance matrix of estimation errors can be avoided without incurring unacceptable system errors. Simulation data for earth orbiting satellites are provided to substantiate this assertion.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Identification of dynamic systems, theory and formulation

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1985-01-01

The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.

Fast maximum likelihood estimation using continuous-time neural point process models.

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural activity involves the computation of a maximum-likelihood estimate. The time to computate this estimate, per neuron, is proportional to the number of bins in a finely spaced discretization of time. By using continuous-time models of neural activity and the optimally efficient Gaussian quadrature, memory requirements and computation times are dramatically decreased in the commonly encountered situation where the number of parameters p is much less than the number of time-bins n. In this regime, with q equal to the quadrature order, memory requirements are decreased from O(np) to O(qp), and the number of floating-point operations are decreased from O(np(2)) to O(qp(2)). Accuracy of the proposed estimates is assessed based upon physiological consideration, error bounds, and mathematical results describing the relation between numerical integration error and numerical error affecting both parameter estimates and the observed Fisher information. A check is provided which is used to adapt the order of numerical integration. The procedure is verified in simulation and for hippocampal recordings. It is found that in 95 % of hippocampal recordings a q of 60 yields numerical error negligible with respect to parameter estimate standard error. Statistical inference using the proposed methodology is a fast and convenient alternative to statistical inference performed using a discrete-time point process model of neural activity. It enables the employment of the statistical methodology available with discrete-time inference, but is faster, uses less memory, and avoids any error due to discretization.

Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers

USGS Publications Warehouse

Runkel, Robert L.; Crawford, Charles G.; Cohn, Timothy A.

2004-01-01

LOAD ESTimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers. Given a time series of streamflow, additional data variables, and constituent concentration, LOADEST assists the user in developing a regression model for the estimation of constituent load (calibration). Explanatory variables within the regression model include various functions of streamflow, decimal time, and additional user-specified data variables. The formulated regression model then is used to estimate loads over a user-specified time interval (estimation). Mean load estimates, standard errors, and 95 percent confidence intervals are developed on a monthly and(or) seasonal basis. The calibration and estimation procedures within LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation (AMLE) and Maximum Likelihood Estimation (MLE), are appropriate when the calibration model errors (residuals) are normally distributed. Of the two, AMLE is the method of choice when the calibration data set (time series of streamflow, additional data variables, and concentration) contains censored data. The third method, Least Absolute Deviation (LAD), is an alternative to maximum likelihood estimation when the residuals are not normally distributed. LOADEST output includes diagnostic tests and warnings to assist the user in determining the appropriate estimation method and in interpreting the estimated loads. This report describes the development and application of LOADEST. Sections of the report describe estimation theory, input/output specifications, sample applications, and installation instructions.

2-Step Maximum Likelihood Channel Estimation for Multicode DS-CDMA with Frequency-Domain Equalization

NASA Astrophysics Data System (ADS)

Kojima, Yohei; Takeda, Kazuaki; Adachi, Fumiyuki

Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can provide better downlink bit error rate (BER) performance of direct sequence code division multiple access (DS-CDMA) than the conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. In this paper, we propose a new 2-step maximum likelihood channel estimation (MLCE) for DS-CDMA with FDE in a very slow frequency-selective fading environment. The 1st step uses the conventional pilot-assisted MMSE-CE and the 2nd step carries out the MLCE using decision feedback from the 1st step. The BER performance improvement achieved by 2-step MLCE over pilot assisted MMSE-CE is confirmed by computer simulation.

Dynamic Programming and Error Estimates for Stochastic Control Problems with Maximum Cost

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bokanowski, Olivier, E-mail: boka@math.jussieu.fr; Picarelli, Athena, E-mail: athena.picarelli@inria.fr; Zidani, Hasnaa, E-mail: hasnaa.zidani@ensta.fr

2015-02-15

This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system ofmore » controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.« less

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

Investigating the Impact of Uncertainty about Item Parameters on Ability Estimation

ERIC Educational Resources Information Center

Zhang, Jinming; Xie, Minge; Song, Xiaolan; Lu, Ting

2011-01-01

Asymptotic expansions of the maximum likelihood estimator (MLE) and weighted likelihood estimator (WLE) of an examinee's ability are derived while item parameter estimators are treated as covariates measured with error. The asymptotic formulae present the amount of bias of the ability estimators due to the uncertainty of item parameter estimators.…

Multipath induced errors in meteorological Doppler/interferometer location systems

NASA Technical Reports Server (NTRS)

Wallace, R. G.

1984-01-01

One application of an RF interferometer aboard a low-orbiting spacecraft to determine the location of ground-based transmitters is in tracking high-altitude balloons for meteorological studies. A source of error in this application is reflection of the signal from the sea surface. Through propagating and signal analysis, the magnitude of the reflection-induced error in both Doppler frequency measurements and interferometer phase measurements was estimated. The theory of diffuse scattering from random surfaces was applied to obtain the power spectral density of the reflected signal. The processing of the combined direct and reflected signals was then analyzed to find the statistics of the measurement error. It was found that the error varies greatly during the satellite overpass and attains its maximum value at closest approach. The maximum values of interferometer phase error and Doppler frequency error found for the system configuration considered were comparable to thermal noise-induced error.

A Bayesian approach to parameter and reliability estimation in the Poisson distribution.

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1972-01-01

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.

An empirical Bayes approach for the Poisson life distribution.

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1973-01-01

A smooth empirical Bayes estimator is derived for the intensity parameter (hazard rate) in the Poisson distribution as used in life testing. The reliability function is also estimated either by using the empirical Bayes estimate of the parameter, or by obtaining the expectation of the reliability function. The behavior of the empirical Bayes procedure is studied through Monte Carlo simulation in which estimates of mean-squared errors of the empirical Bayes estimators are compared with those of conventional estimators such as minimum variance unbiased or maximum likelihood. Results indicate a significant reduction in mean-squared error of the empirical Bayes estimators over the conventional variety.

Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

PubMed Central

Zhang, Tangtang; Wen, Jun; van der Velde, Rogier; Meng, Xianhong; Li, Zhenchao; Liu, Yuanyong; Liu, Rong

2008-01-01

The total atmospheric water vapor content (TAWV) and land surface temperature (LST) play important roles in meteorology, hydrology, ecology and some other disciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track Scanning Radiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateau in China by using a practical split window algorithm. The distribution of the TAWV is accord with that of the MODIS TAWV products, which indicates that the estimation of the total atmospheric water vapor content is reliable. Validations of the LST by comparing with the ground measurements indicate that the maximum absolute derivation, the maximum relative error and the average relative error is 4.0K, 11.8% and 5.0% respectively, which shows that the retrievals are believable; this algorithm can provide a new way to estimate the LST from AATSR data. PMID:27879795

Algorithm for pose estimation based on objective function with uncertainty-weighted measuring error of feature point cling to the curved surface.

PubMed

Huo, Ju; Zhang, Guiyang; Yang, Ming

2018-04-20

This paper is concerned with the anisotropic and non-identical gray distribution of feature points clinging to the curved surface, upon which a high precision and uncertainty-resistance algorithm for pose estimation is proposed. Weighted contribution of uncertainty to the objective function of feature points measuring error is analyzed. Then a novel error objective function based on the spatial collinear error is constructed by transforming the uncertainty into a covariance-weighted matrix, which is suitable for the practical applications. Further, the optimized generalized orthogonal iterative (GOI) algorithm is utilized for iterative solutions such that it avoids the poor convergence and significantly resists the uncertainty. Hence, the optimized GOI algorithm extends the field-of-view applications and improves the accuracy and robustness of the measuring results by the redundant information. Finally, simulation and practical experiments show that the maximum error of re-projection image coordinates of the target is less than 0.110 pixels. Within the space 3000  mm×3000  mm×4000  mm, the maximum estimation errors of static and dynamic measurement for rocket nozzle motion are superior to 0.065° and 0.128°, respectively. Results verify the high accuracy and uncertainty attenuation performance of the proposed approach and should therefore have potential for engineering applications.

Estimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error

PubMed Central

Liu, Xiaoming; Fu, Yun-Xin; Maxwell, Taylor J.; Boerwinkle, Eric

2010-01-01

It is known that sequencing error can bias estimation of evolutionary or population genetic parameters. This problem is more prominent in deep resequencing studies because of their large sample size n, and a higher probability of error at each nucleotide site. We propose a new method based on the composite likelihood of the observed SNP configurations to infer population mutation rate θ = 4Neμ, population exponential growth rate R, and error rate ɛ, simultaneously. Using simulation, we show the combined effects of the parameters, θ, n, ɛ, and R on the accuracy of parameter estimation. We compared our maximum composite likelihood estimator (MCLE) of θ with other θ estimators that take into account the error. The results show the MCLE performs well when the sample size is large or the error rate is high. Using parametric bootstrap, composite likelihood can also be used as a statistic for testing the model goodness-of-fit of the observed DNA sequences. The MCLE method is applied to sequence data on the ANGPTL4 gene in 1832 African American and 1045 European American individuals. PMID:19952140

Measurement Model Specification Error in LISREL Structural Equation Models.

ERIC Educational Resources Information Center

Baldwin, Beatrice; Lomax, Richard

This LISREL study examines the robustness of the maximum likelihood estimates under varying degrees of measurement model misspecification. A true model containing five latent variables (two endogenous and three exogenous) and two indicator variables per latent variable was used. Measurement model misspecification considered included errors of…

Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

PubMed Central

Park, Chan Gook

2018-01-01

An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU. In order to obtain the course measurements, the filter uses magnetic sensors and a position-trace based course angle. For preventing magnetic disturbance from contaminating the estimation, the magnetic sensors are attached to the waistband. Because the course angle error is mainly due to the heading error of the IMU, and the characteristic error of the heading angle is highly dependent on that of the course angle, the estimated course angle error is used as a measurement for estimating the heading error in the second stage filter. At the second stage, an inertial navigation system-extended Kalman filter-ZUPT (INS-EKF-ZUPT) method is adopted. As the heading error is estimated directly by using course-angle error measurements, the estimation accuracy for the heading and yaw gyro bias can be enhanced, compared with the ZUPT-only case, which eventually enhances the position accuracy more efficiently. The performance enhancements are verified via experiments, and the way-point position error for the proposed method is compared with those for the ZUPT-only case and with other cases that use ZUPT and various types of magnetic heading measurements. The results show that the position errors are reduced by a maximum of 90% compared with the conventional ZUPT based PDR algorithms. PMID:29690539

Monte Carlo studies of ocean wind vector measurements by SCATT: Objective criteria and maximum likelihood estimates for removal of aliases, and effects of cell size on accuracy of vector winds

NASA Technical Reports Server (NTRS)

Pierson, W. J.

1982-01-01

The scatterometer on the National Oceanic Satellite System (NOSS) is studied by means of Monte Carlo techniques so as to determine the effect of two additional antennas for alias (or ambiguity) removal by means of an objective criteria technique and a normalized maximum likelihood estimator. Cells nominally 10 km by 10 km, 10 km by 50 km, and 50 km by 50 km are simulated for winds of 4, 8, 12 and 24 m/s and incidence angles of 29, 39, 47, and 53.5 deg for 15 deg changes in direction. The normalized maximum likelihood estimate (MLE) is correct a large part of the time, but the objective criterion technique is recommended as a reserve, and more quickly computed, procedure. Both methods for alias removal depend on the differences in the present model function at upwind and downwind. For 10 km by 10 km cells, it is found that the MLE method introduces a correlation between wind speed errors and aspect angle (wind direction) errors that can be as high as 0.8 or 0.9 and that the wind direction errors are unacceptably large, compared to those obtained for the SASS for similar assumptions.

Dual-energy X-ray absorptiometry: analysis of pediatric fat estimate errors due to tissue hydration effects.

PubMed

Testolin, C G; Gore, R; Rivkin, T; Horlick, M; Arbo, J; Wang, Z; Chiumello, G; Heymsfield, S B

2000-12-01

Dual-energy X-ray absorptiometry (DXA) percent (%) fat estimates may be inaccurate in young children, who typically have high tissue hydration levels. This study was designed to provide a comprehensive analysis of pediatric tissue hydration effects on DXA %fat estimates. Phase 1 was experimental and included three in vitro studies to establish the physical basis of DXA %fat-estimation models. Phase 2 extended phase 1 models and consisted of theoretical calculations to estimate the %fat errors emanating from previously reported pediatric hydration effects. Phase 1 experiments supported the two-compartment DXA soft tissue model and established that pixel ratio of low to high energy (R values) are a predictable function of tissue elemental content. In phase 2, modeling of reference body composition values from birth to age 120 mo revealed that %fat errors will arise if a "constant" adult lean soft tissue R value is applied to the pediatric population; the maximum %fat error, approximately 0.8%, would be present at birth. High tissue hydration, as observed in infants and young children, leads to errors in DXA %fat estimates. The magnitude of these errors based on theoretical calculations is small and may not be of clinical or research significance.

Maximum likelihood techniques applied to quasi-elastic light scattering

NASA Technical Reports Server (NTRS)

Edwards, Robert V.

1992-01-01

There is a necessity of having an automatic procedure for reliable estimation of the quality of the measurement of particle size from QELS (Quasi-Elastic Light Scattering). Getting the measurement itself, before any error estimates can be made, is a problem because it is obtained by a very indirect measurement of a signal derived from the motion of particles in the system and requires the solution of an inverse problem. The eigenvalue structure of the transform that generates the signal is such that an arbitrarily small amount of noise can obliterate parts of any practical inversion spectrum. This project uses the Maximum Likelihood Estimation (MLE) as a framework to generate a theory and a functioning set of software to oversee the measurement process and extract the particle size information, while at the same time providing error estimates for those measurements. The theory involved verifying a correct form of the covariance matrix for the noise on the measurement and then estimating particle size parameters using a modified histogram approach.

Determinants of Standard Errors of MLEs in Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Cheng, Ying; Zhang, Wei

2010-01-01

This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found.…

Maximum likelihood phase-retrieval algorithm: applications.

PubMed

Nahrstedt, D A; Southwell, W H

1984-12-01

The maximum likelihood estimator approach is shown to be effective in determining the wave front aberration in systems involving laser and flow field diagnostics and optical testing. The robustness of the algorithm enables convergence even in cases of severe wave front error and real, nonsymmetrical, obscured amplitude distributions.

Comparison of stability and control parameters for a light, single-engine, high-winged aircraft using different flight test and parameter estimation techniques

NASA Technical Reports Server (NTRS)

Suit, W. T.; Cannaday, R. L.

1979-01-01

The longitudinal and lateral stability and control parameters for a high wing, general aviation, airplane are examined. Estimations using flight data obtained at various flight conditions within the normal range of the aircraft are presented. The estimations techniques, an output error technique (maximum likelihood) and an equation error technique (linear regression), are presented. The longitudinal static parameters are estimated from climbing, descending, and quasi steady state flight data. The lateral excitations involve a combination of rudder and ailerons. The sensitivity of the aircraft modes of motion to variations in the parameter estimates are discussed.

Exploiting Measurement Uncertainty Estimation in Evaluation of GOES-R ABI Image Navigation Accuracy Using Image Registration Techniques

NASA Technical Reports Server (NTRS)

Haas, Evan; DeLuccia, Frank

2016-01-01

In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.

Motion-induced phase error estimation and correction in 3D diffusion tensor imaging.

PubMed

Van, Anh T; Hernando, Diego; Sutton, Bradley P

2011-11-01

A multishot data acquisition strategy is one way to mitigate B0 distortion and T2∗ blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method.

Correcting for sequencing error in maximum likelihood phylogeny inference.

PubMed

Kuhner, Mary K; McGill, James

2014-11-04

Accurate phylogenies are critical to taxonomy as well as studies of speciation processes and other evolutionary patterns. Accurate branch lengths in phylogenies are critical for dating and rate measurements. Such accuracy may be jeopardized by unacknowledged sequencing error. We use simulated data to test a correction for DNA sequencing error in maximum likelihood phylogeny inference. Over a wide range of data polymorphism and true error rate, we found that correcting for sequencing error improves recovery of the branch lengths, even if the assumed error rate is up to twice the true error rate. Low error rates have little effect on recovery of the topology. When error is high, correction improves topological inference; however, when error is extremely high, using an assumed error rate greater than the true error rate leads to poor recovery of both topology and branch lengths. The error correction approach tested here was proposed in 2004 but has not been widely used, perhaps because researchers do not want to commit to an estimate of the error rate. This study shows that correction with an approximate error rate is generally preferable to ignoring the issue. Copyright © 2014 Kuhner and McGill.

Generalized site occupancy models allowing for false positive and false negative errors

USGS Publications Warehouse

Royle, J. Andrew; Link, W.A.

2006-01-01

Site occupancy models have been developed that allow for imperfect species detection or ?false negative? observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that ?false positive? errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.

Assessment of Thematic Mapper Band-to-band Registration by the Block Correlation Method

NASA Technical Reports Server (NTRS)

Card, D. H.; Wrigley, R. C.; Mertz, F. C.; Hall, J. R.

1984-01-01

The design of the Thematic Mapper (TM) multispectral radiometer makes it susceptible to band-to-band misregistration. To estimate band-to-band misregistration a block correlation method is employed. This method is chosen over other possible techniques (band differencing and flickering) because quantitative results are produced. The method correlates rectangular blocks of pixels from one band against blocks centered on identical pixels from a second band. The block pairs are shifted in pixel increments both vertically and horizontally with respect to each other and the correlation coefficient for each shift position is computed. The displacement corresponding to the maximum correlation is taken as the best estimate of registration error for each block pair. Subpixel shifts are estimated by a bi-quadratic interpolation of the correlation values surrounding the maximum correlation. To obtain statistical summaries for each band combination post processing of the block correlation results performed. The method results in estimates of registration error that are consistent with expectations.

The role of misclassification in estimating proportions and an estimator of misclassification probability

Treesearch

Patrick L. Zimmerman; Greg C. Liknes

2010-01-01

Dot grids are often used to estimate the proportion of land cover belonging to some class in an aerial photograph. Interpreter misclassification is an often-ignored source of error in dot-grid sampling that has the potential to significantly bias proportion estimates. For the case when the true class of items is unknown, we present a maximum-likelihood estimator of...

Bradley Fighting Vehicle Gunnery: An Analysis of Engagement Strategies for the M242 25-mm Automatic Gun

DTIC Science & Technology

1993-03-01

source for this estimate of eight rounds per BMP target. According to analyst Donna Quirido, AMSAA does not provide or support any such estimate (30...engagement or in the case of. the Bradley, stabilization inaccuracies. According to Helgert: These errors give rise to aim-wander, a term that derives from...the same area. (6:14_5) The resulting approximation to the truncated normal integral has a maximum relative error of 0.0075. Using Polya -Williams, an

Streamflow simulation studies of the Hillsborough, Alafia, and Anclote Rivers, west-central Florida

USGS Publications Warehouse

Turner, J.F.

1979-01-01

A modified version of the Georgia Tech Watershed Model was applied for the purpose of flow simulation in three large river basins of west-central Florida. Calibrations were evaluated by comparing the following synthesized and observed data: annual hydrographs for the 1959, 1960, 1973 and 1974 water years, flood hydrographs (maximum daily discharge and flood volume), and long-term annual flood-peak discharges (1950-72). Annual hydrographs, excluding the 1973 water year, were compared using average absolute error in annual runoff and daily flows and correlation coefficients of monthly and daily flows. Correlations coefficients for simulated and observed maximum daily discharges and flood volumes used for calibrating range from 0.91 to 0.98 and average standard errors of estimate range from 18 to 45 percent. Correlation coefficients for simulated and observed annual flood-peak discharges range from 0.60 to 0.74 and average standard errors of estimate range from 33 to 44 percent. (Woodard-USGS)

The influence of SO4 and NO3 to the acidity (pH) of rainwater using minimum variance quadratic unbiased estimation (MIVQUE) and maximum likelihood methods

NASA Astrophysics Data System (ADS)

Dilla, Shintia Ulfa; Andriyana, Yudhie; Sudartianto

2017-03-01

Acid rain causes many bad effects in life. It is formed by two strong acids, sulfuric acid (H2SO4) and nitric acid (HNO3), where sulfuric acid is derived from SO2 and nitric acid from NOx {x=1,2}. The purpose of the research is to find out the influence of So4 and NO3 levels contained in the rain to the acidity (pH) of rainwater. The data are incomplete panel data with two-way error component model. The panel data is a collection of some of the observations that observed from time to time. It is said incomplete if each individual has a different amount of observation. The model used in this research is in the form of random effects model (REM). Minimum variance quadratic unbiased estimation (MIVQUE) is used to estimate the variance error components, while maximum likelihood estimation is used to estimate the parameters. As a result, we obtain the following model: Ŷ* = 0.41276446 - 0.00107302X1 + 0.00215470X2.

Consequences of Secondary Calibrations on Divergence Time Estimates.

PubMed

Schenk, John J

2016-01-01

Secondary calibrations (calibrations based on the results of previous molecular dating studies) are commonly applied in divergence time analyses in groups that lack fossil data; however, the consequences of applying secondary calibrations in a relaxed-clock approach are not fully understood. I tested whether applying the posterior estimate from a primary study as a prior distribution in a secondary study results in consistent age and uncertainty estimates. I compared age estimates from simulations with 100 randomly replicated secondary trees. On average, the 95% credible intervals of node ages for secondary estimates were significantly younger and narrower than primary estimates. The primary and secondary age estimates were significantly different in 97% of the replicates after Bonferroni corrections. Greater error in magnitude was associated with deeper than shallower nodes, but the opposite was found when standardized by median node age, and a significant positive relationship was determined between the number of tips/age of secondary trees and the total amount of error. When two secondary calibrated nodes were analyzed, estimates remained significantly different, and although the minimum and median estimates were associated with less error, maximum age estimates and credible interval widths had greater error. The shape of the prior also influenced error, in which applying a normal, rather than uniform, prior distribution resulted in greater error. Secondary calibrations, in summary, lead to a false impression of precision and the distribution of age estimates shift away from those that would be inferred by the primary analysis. These results suggest that secondary calibrations should not be applied as the only source of calibration in divergence time analyses that test time-dependent hypotheses until the additional error associated with secondary calibrations is more properly modeled to take into account increased uncertainty in age estimates.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

Using digital inpainting to estimate incident light intensity for the calculation of red blood cell oxygen saturation from microscopy images.

PubMed

Sové, Richard J; Drakos, Nicole E; Fraser, Graham M; Ellis, Christopher G

2018-05-25

Red blood cell oxygen saturation is an important indicator of oxygen supply to tissues in the body. Oxygen saturation can be measured by taking advantage of spectroscopic properties of hemoglobin. When this technique is applied to transmission microscopy, the calculation of saturation requires determination of incident light intensity at each pixel occupied by the red blood cell; this value is often approximated from a sequence of images as the maximum intensity over time. This method often fails when the red blood cells are moving too slowly, or if hematocrit is too large since there is not a large enough gap between the cells to accurately calculate the incident intensity value. A new method of approximating incident light intensity is proposed using digital inpainting. This novel approach estimates incident light intensity with an average percent error of approximately 3%, which exceeds the accuracy of the maximum intensity based method in most cases. The error in incident light intensity corresponds to a maximum error of approximately 2% saturation. Therefore, though this new method is computationally more demanding than the traditional technique, it can be used in cases where the maximum intensity-based method fails (e.g. stationary cells), or when higher accuracy is required. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception.

PubMed

Shimansky, Y P

2011-05-01

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

Fast maximum likelihood estimation of mutation rates using a birth-death process.

PubMed

Wu, Xiaowei; Zhu, Hongxiao

2015-02-07

Since fluctuation analysis was first introduced by Luria and Delbrück in 1943, it has been widely used to make inference about spontaneous mutation rates in cultured cells. Under certain model assumptions, the probability distribution of the number of mutants that appear in a fluctuation experiment can be derived explicitly, which provides the basis of mutation rate estimation. It has been shown that, among various existing estimators, the maximum likelihood estimator usually demonstrates some desirable properties such as consistency and lower mean squared error. However, its application in real experimental data is often hindered by slow computation of likelihood due to the recursive form of the mutant-count distribution. We propose a fast maximum likelihood estimator of mutation rates, MLE-BD, based on a birth-death process model with non-differential growth assumption. Simulation studies demonstrate that, compared with the conventional maximum likelihood estimator derived from the Luria-Delbrück distribution, MLE-BD achieves substantial improvement on computational speed and is applicable to arbitrarily large number of mutants. In addition, it still retains good accuracy on point estimation. Published by Elsevier Ltd.

Building a kinetic Monte Carlo model with a chosen accuracy.

PubMed

Bhute, Vijesh J; Chatterjee, Abhijit

2013-06-28

The kinetic Monte Carlo (KMC) method is a popular modeling approach for reaching large materials length and time scales. The KMC dynamics is erroneous when atomic processes that are relevant to the dynamics are missing from the KMC model. Recently, we had developed for the first time an error measure for KMC in Bhute and Chatterjee [J. Chem. Phys. 138, 084103 (2013)]. The error measure, which is given in terms of the probability that a missing process will be selected in the correct dynamics, requires estimation of the missing rate. In this work, we present an improved procedure for estimating the missing rate. The estimate found using the new procedure is within an order of magnitude of the correct missing rate, unlike our previous approach where the estimate was larger by orders of magnitude. This enables one to find the error in the KMC model more accurately. In addition, we find the time for which the KMC model can be used before a maximum error in the dynamics has been reached.

Effects of Employing Ridge Regression in Structural Equation Models.

ERIC Educational Resources Information Center

McQuitty, Shaun

1997-01-01

LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. Implications of the ridge option for model fit, parameter estimates, and standard errors are explored through two examples. (SLD)

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles

PubMed Central

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-01-01

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists. PMID:27548183

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

PubMed

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-08-19

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA

PubMed Central

Fosdick, Bailey K.; Hoff, Peter D.

2014-01-01

Human mortality data sets can be expressed as multiway data arrays, the dimensions of which correspond to categories by which mortality rates are reported, such as age, sex, country and year. Regression models for such data typically assume an independent error distribution or an error model that allows for dependence along at most one or two dimensions of the data array. However, failing to account for other dependencies can lead to inefficient estimates of regression parameters, inaccurate standard errors and poor predictions. An alternative to assuming independent errors is to allow for dependence along each dimension of the array using a separable covariance model. However, the number of parameters in this model increases rapidly with the dimensions of the array and, for many arrays, maximum likelihood estimates of the covariance parameters do not exist. In this paper, we propose a submodel of the separable covariance model that estimates the covariance matrix for each dimension as having factor analytic structure. This model can be viewed as an extension of factor analysis to array-valued data, as it uses a factor model to estimate the covariance along each dimension of the array. We discuss properties of this model as they relate to ordinary factor analysis, describe maximum likelihood and Bayesian estimation methods, and provide a likelihood ratio testing procedure for selecting the factor model ranks. We apply this methodology to the analysis of data from the Human Mortality Database, and show in a cross-validation experiment how it outperforms simpler methods. Additionally, we use this model to impute mortality rates for countries that have no mortality data for several years. Unlike other approaches, our methodology is able to estimate similarities between the mortality rates of countries, time periods and sexes, and use this information to assist with the imputations. PMID:25489353

A Monte Carlo comparison of the recovery of winds near upwind and downwind from the SASS-1 model function by means of the sum of squares algorithm and a maximum likelihood estimator

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1984-01-01

Backscatter measurements at upwind and crosswind are simulated for five incidence angles by means of the SASS-1 model function. The effects of communication noise and attitude errors are simulated by Monte Carlo methods, and the winds are recovered by both the Sum of Square (SOS) algorithm and a Maximum Likelihood Estimater (MLE). The SOS algorithm is shown to fail for light enough winds at all incidence angles and to fail to show areas of calm because backscatter estimates that were negative or that produced incorrect values of K sub p greater than one were discarded. The MLE performs well for all input backscatter estimates and returns calm when both are negative. The use of the SOS algorithm is shown to have introduced errors in the SASS-1 model function that, in part, cancel out the errors that result from using it, but that also cause disagreement with other data sources such as the AAFE circle flight data at light winds. Implications for future scatterometer systems are given.

An introduction of component fusion extend Kalman filtering method

NASA Astrophysics Data System (ADS)

Geng, Yue; Lei, Xusheng

2018-05-01

In this paper, the Component Fusion Extend Kalman Filtering (CFEKF) algorithm is proposed. Assuming each component of error propagation are independent with Gaussian distribution. The CFEKF can be obtained through the maximum likelihood of propagation error, which can adjust the state transition matrix and the measured matrix adaptively. With minimize linearization error, CFEKF can an effectively improve the estimation accuracy of nonlinear system state. The computation of CFEKF is similar to EKF which is easy for application.

Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

NASA Technical Reports Server (NTRS)

Tranter, W. H.; Turner, M. D.

1977-01-01

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation.

Estimation of the sea surface's two-scale backscatter parameters

NASA Technical Reports Server (NTRS)

Wentz, F. J.

1978-01-01

The relationship between the sea-surface normalized radar cross section and the friction velocity vector is determined using a parametric two-scale scattering model. The model parameters are found from a nonlinear maximum likelihood estimation. The estimation is based on aircraft scatterometer measurements and the sea-surface anemometer measurements collected during the JONSWAP '75 experiment. The estimates of the ten model parameters converge to realistic values that are in good agreement with the available oceanographic data. The rms discrepancy between the model and the cross section measurements is 0.7 db, which is the rms sum of a 0.3 db average measurement error and a 0.6 db modeling error.

On-line estimation of error covariance parameters for atmospheric data assimilation

NASA Technical Reports Server (NTRS)

Dee, Dick P.

1995-01-01

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

DOE PAGES

Ye, Xin; Garikapati, Venu M.; You, Daehyun; ...

2017-11-08

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basismore » of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.« less

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ye, Xin; Garikapati, Venu M.; You, Daehyun

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basismore » of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.« less

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

PubMed

Schmidt, Taly Gilat; Wang, Adam S; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-10-01

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm

PubMed Central

Schmidt, Taly Gilat; Wang, Adam S.; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-01-01

Abstract. The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was −7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors. PMID:27921070

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography - Part II (Application).

PubMed

Claumann, Carlos Alberto; Wüst Zibetti, André; Bolzan, Ariovaldo; Machado, Ricardo A F; Pinto, Leonel Teixeira

2015-12-18

For this work, an analysis of parameter estimation for the retention factor in GC model was performed, considering two different criteria: sum of square error, and maximum error in absolute value; relevant statistics are described for each case. The main contribution of this work is the implementation of an initialization scheme (specialized) for the estimated parameters, which features fast convergence (low computational time) and is based on knowledge of the surface of the error criterion. In an application to a series of alkanes, specialized initialization resulted in significant reduction to the number of evaluations of the objective function (reducing computational time) in the parameter estimation. The obtained reduction happened between one and two orders of magnitude, compared with the simple random initialization. Copyright © 2015 Elsevier B.V. All rights reserved.

Statistical models for estimating daily streamflow in Michigan

USGS Publications Warehouse

Holtschlag, D.J.; Salehi, Habib

1992-01-01

Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l < 2 days and l < 9 days, respectively. Composite estimates were computed as a weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l < 32 days. In addition, the composite estimates ensure a gradual transition between periods of estimated and measured flows. Model performance among stations of differing model error magnitudes were compared by computing ratios of the mean standard deviation of the length l composite errors to the standard deviation of OLSR errors. The mean error ratio for the set of 25 selected stations was less than 1 for intervals l < 32 days. Considering the frequency characteristics of the length of intervals of estimated record in Michigan, the effective mean error ratio for intervals < 30 days was 0.52. Thus, for intervals of estimation of 1 month or less, the error of the composite estimate is substantially lower than error of the OLSR estimate.

Dynamic Modeling Accuracy Dependence on Errors in Sensor Measurements, Mass Properties, and Aircraft Geometry

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2013-01-01

A nonlinear simulation of the NASA Generic Transport Model was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of dynamic models identified from flight data. Measurements from a typical system identification maneuver were systematically and progressively deteriorated and then used to estimate stability and control derivatives within a Monte Carlo analysis. Based on the results, recommendations were provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using other flight conditions, parameter estimation methods, and a full-scale F-16 nonlinear aircraft simulation were compared with these recommendations.

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors

USGS Publications Warehouse

Langbein, John O.

2017-01-01

Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise, 1/fα">1/fα1/fα with frequency, f. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. (J Geod, 2013. doi:10.1007/s00190-012-0605-0) demonstrate one technique that substantially increases the efficiency of the MLE methods, yet is only an approximate solution for power-law indices >1.0 since they require the data covariance matrix to be Toeplitz. That restriction can be removed by simply forming a data filter that adds noise processes rather than combining them in quadrature. Consequently, the inversion of the data covariance matrix is simplified yet provides robust results for a wider range of power-law indices.

Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors

NASA Astrophysics Data System (ADS)

Langbein, John

2017-08-01

Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise, 1/f^{α } with frequency, f. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. (J Geod, 2013. doi: 10.1007/s00190-012-0605-0) demonstrate one technique that substantially increases the efficiency of the MLE methods, yet is only an approximate solution for power-law indices >1.0 since they require the data covariance matrix to be Toeplitz. That restriction can be removed by simply forming a data filter that adds noise processes rather than combining them in quadrature. Consequently, the inversion of the data covariance matrix is simplified yet provides robust results for a wider range of power-law indices.

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion.

PubMed

Yi, Dong-Hoon; Lee, Tae-Jae; Cho, Dong-Il Dan

2018-01-10

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

PubMed Central

Yi, Dong-Hoon; Lee, Tae-Jae; Cho, Dong-Il “Dan”

2018-01-01

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively. PMID:29320414

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Synergies in Astrometry: Predicting Navigational Error of Visual Binary Stars

NASA Astrophysics Data System (ADS)

Gessner Stewart, Susan

2015-08-01

Celestial navigation can employ a number of bright stars which are in binary systems. Often these are unresolved, appearing as a single, center-of-light object. A number of these systems are, however, in wide systems which could introduce a margin of error in the navigation solution if not handled properly. To illustrate the importance of good orbital solutions for binary systems - as well as good astrometry in general - the relationship between the center-of-light versus individual catalog position of celestial bodies and the error in terrestrial position derived via celestial navigation is demonstrated. From the list of navigational binary stars, fourteen such binary systems with at least 3.0 arcseconds apparent separation are explored. Maximum navigational error is estimated under the assumption that the bright star in the pair is observed at maximum separation, but the center-of-light is employed in the navigational solution. The relationships between navigational error and separation, orbital periods, and observers' latitude are discussed.

Fast and accurate estimation of the covariance between pairwise maximum likelihood distances.

PubMed

Gil, Manuel

2014-01-01

Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares or combines distances. This paper introduces a fast estimator for the covariance of two pairwise maximum likelihood distances, estimated under general Markov models. The estimator is based on a conjecture (going back to Nei & Jin, 1989) which links the covariance to path lengths. It is proven here under a simple symmetric substitution model. A simulation shows that the estimator outperforms previously published ones in terms of the mean squared error.

Fast and accurate estimation of the covariance between pairwise maximum likelihood distances

PubMed Central

2014-01-01

Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares or combines distances. This paper introduces a fast estimator for the covariance of two pairwise maximum likelihood distances, estimated under general Markov models. The estimator is based on a conjecture (going back to Nei & Jin, 1989) which links the covariance to path lengths. It is proven here under a simple symmetric substitution model. A simulation shows that the estimator outperforms previously published ones in terms of the mean squared error. PMID:25279263

Estimation of perspective errors in 2D2C-PIV measurements for 3D concentrated vortices

NASA Astrophysics Data System (ADS)

Ma, Bao-Feng; Jiang, Hong-Gang

2018-06-01

Two-dimensional planar PIV (2D2C) is still extensively employed in flow measurement owing to its availability and reliability, although more advanced PIVs have been developed. It has long been recognized that there exist perspective errors in velocity fields when employing the 2D2C PIV to measure three-dimensional (3D) flows, the magnitude of which depends on out-of-plane velocity and geometric layouts of the PIV. For a variety of vortex flows, however, the results are commonly represented by vorticity fields, instead of velocity fields. The present study indicates that the perspective error in vorticity fields relies on gradients of the out-of-plane velocity along a measurement plane, instead of the out-of-plane velocity itself. More importantly, an estimation approach to the perspective error in 3D vortex measurements was proposed based on a theoretical vortex model and an analysis on physical characteristics of the vortices, in which the gradient of out-of-plane velocity is uniquely determined by the ratio of the maximum out-of-plane velocity to maximum swirling velocity of the vortex; meanwhile, the ratio has upper limits for naturally formed vortices. Therefore, if the ratio is imposed with the upper limits, the perspective error will only rely on the geometric layouts of PIV that are known in practical measurements. Using this approach, the upper limits of perspective errors of a concentrated vortex can be estimated for vorticity and other characteristic quantities of the vortex. In addition, the study indicates that the perspective errors in vortex location, vortex strength, and vortex radius can be all zero for axisymmetric vortices if they are calculated by proper methods. The dynamic mode decomposition on an oscillatory vortex indicates that the perspective errors of each DMD mode are also only dependent on the gradient of out-of-plane velocity if the modes are represented by vorticity.

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

NASA Astrophysics Data System (ADS)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

Accuracy assessment of high-rate GPS measurements for seismology

NASA Astrophysics Data System (ADS)

Elosegui, P.; Davis, J. L.; Ekström, G.

2007-12-01

Analysis of GPS measurements with a controlled laboratory system, built to simulate the ground motions caused by tectonic earthquakes and other transient geophysical signals such as glacial earthquakes, enables us to assess the technique of high-rate GPS. The root-mean-square (rms) position error of this system when undergoing realistic simulated seismic motions is 0.05~mm, with maximum position errors of 0.1~mm, thus providing "ground truth" GPS displacements. We have acquired an extensive set of high-rate GPS measurements while inducing seismic motions on a GPS antenna mounted on this system with a temporal spectrum similar to real seismic events. We found that, for a particular 15-min-long test event, the rms error of the 1-Hz GPS position estimates was 2.5~mm, with maximum position errors of 10~mm, and the error spectrum of the GPS estimates was approximately flicker noise. These results may however represent a best-case scenario since they were obtained over a short (~10~m) baseline, thereby greatly mitigating baseline-dependent errors, and when the number and distribution of satellites on the sky was good. For example, we have determined that the rms error can increase by a factor of 2--3 as the GPS constellation changes throughout the day, with an average value of 3.5~mm for eight identical, hourly-spaced, consecutive test events. The rms error also increases with increasing baseline, as one would expect, with an average rms error for a ~1400~km baseline of 9~mm. We will present an assessment of the accuracy of high-rate GPS based on these measurements, discuss the implications of this study for seismology, and describe new applications in glaciology.

Dependence of Dynamic Modeling Accuracy on Sensor Measurements, Mass Properties, and Aircraft Geometry

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2013-01-01

The NASA Generic Transport Model (GTM) nonlinear simulation was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of identified parameters in mathematical models describing the flight dynamics and determined from flight data. Measurements from a typical flight condition and system identification maneuver were systematically and progressively deteriorated by introducing noise, resolution errors, and bias errors. The data were then used to estimate nondimensional stability and control derivatives within a Monte Carlo simulation. Based on these results, recommendations are provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using additional flight conditions and parameter estimation methods, as well as a nonlinear flight simulation of the General Dynamics F-16 aircraft, were compared with these recommendations

Estimation of viscous dissipation in nanodroplet impact and spreading

NASA Astrophysics Data System (ADS)

Li, Xin-Hao; Zhang, Xiang-Xiong; Chen, Min

2015-05-01

The developments in nanocoating and nanospray technology have resulted in the increasing importance of the impact of micro-/nanoscale liquid droplets on solid surface. In this paper, the impact of a nanodroplet on a flat solid surface is examined using molecular dynamics simulations. The impact velocity ranges from 58 m/s to 1044 m/s, in accordance with the Weber number ranging from 0.62 to 200.02 and the Reynolds number ranging from 0.89 to 16.14. The obtained maximum spreading factors are compared with previous models in the literature. The predicted results from the previous models largely deviate from our simulation results, with mean relative errors up to 58.12%. The estimated viscous dissipation is refined to present a modified theoretical model, which reduces the mean relative error to 15.12% in predicting the maximum spreading factor for cases of nanodroplet impact.

Fisher, Sir Ronald Aylmer (1890-1962)

NASA Astrophysics Data System (ADS)

Murdin, P.

2000-11-01

Statistician, born in London, England. After studying astronomy using AIRY's manual on the Theory of Errors he became interested in statistics, and laid the foundation of randomization in experimental design, the analysis of variance and the use of data in estimating the properties of the parent population from which it was drawn. Invented the maximum likelihood method for estimating from random ...

Closed-loop carrier phase synchronization techniques motivated by likelihood functions

NASA Technical Reports Server (NTRS)

Tsou, H.; Hinedi, S.; Simon, M.

1994-01-01

This article reexamines the notion of closed-loop carrier phase synchronization motivated by the theory of maximum a posteriori phase estimation with emphasis on the development of new structures based on both maximum-likelihood and average-likelihood functions. The criterion of performance used for comparison of all the closed-loop structures discussed is the mean-squared phase error for a fixed-loop bandwidth.

A dual-phantom system for validation of velocity measurements in stenosis models under steady flow.

PubMed

Blake, James R; Easson, William J; Hoskins, Peter R

2009-09-01

A dual-phantom system is developed for validation of velocity measurements in stenosis models. Pairs of phantoms with identical geometry and flow conditions are manufactured, one for ultrasound and one for particle image velocimetry (PIV). The PIV model is made from silicone rubber, and a new PIV fluid is made that matches the refractive index of 1.41 of silicone. Dynamic scaling was performed to correct for the increased viscosity of the PIV fluid compared with that of the ultrasound blood mimic. The degree of stenosis in the models pairs agreed to less than 1%. The velocities in the laminar flow region up to the peak velocity location agreed to within 15%, and the difference could be explained by errors in ultrasound velocity estimation. At low flow rates and in mild stenoses, good agreement was observed in the distal flow fields, excepting the maximum velocities. At high flow rates, there was considerable difference in velocities in the poststenosis flow field (maximum centreline differences of 30%), which would seem to represent real differences in hydrodynamic behavior between the two models. Sources of error included: variation of viscosity because of temperature (random error, which could account for differences of up to 7%); ultrasound velocity estimation errors (systematic errors); and geometry effects in each model, particularly because of imperfect connectors and corners (systematic errors, potentially affecting the inlet length and flow stability). The current system is best placed to investigate measurement errors in the laminar flow region rather than the poststenosis turbulent flow region.

Estimation in a discrete tail rate family of recapture sampling models

NASA Technical Reports Server (NTRS)

Gupta, Rajan; Lee, Larry D.

1990-01-01

In the context of recapture sampling design for debugging experiments the problem of estimating the error or hitting rate of the faults remaining in a system is considered. Moment estimators are derived for a family of models in which the rate parameters are assumed proportional to the tail probabilities of a discrete distribution on the positive integers. The estimators are shown to be asymptotically normal and fully efficient. Their fixed sample properties are compared, through simulation, with those of the conditional maximum likelihood estimators.

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

NASA Astrophysics Data System (ADS)

Dong, Sheng; Chi, Kun; Zhang, Qiyi; Zhang, Xiangdong

2012-03-01

Compared with traditional real-time forecasting, this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Grey System and Markov theory into a higher precision model. The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values, and thus gives forecast results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1, 1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years' data are regarded as a hydro-sequence. Eight groups of simulated results show reasonable agreement between the predicted values and the measured data. The GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Maximum likelihood estimation for Cox's regression model under nested case-control sampling.

PubMed

Scheike, Thomas H; Juul, Anders

2004-04-01

Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin-like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.

A Review of System Identification Methods Applied to Aircraft

NASA Technical Reports Server (NTRS)

Klein, V.

1983-01-01

Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.

Methods for estimation of radiation risk in epidemiological studies accounting for classical and Berkson errors in doses.

PubMed

Kukush, Alexander; Shklyar, Sergiy; Masiuk, Sergii; Likhtarov, Illya; Kovgan, Lina; Carroll, Raymond J; Bouville, Andre

2011-02-16

With a binary response Y, the dose-response model under consideration is logistic in flavor with pr(Y=1 | D) = R (1+R)(-1), R = λ(0) + EAR D, where λ(0) is the baseline incidence rate and EAR is the excess absolute risk per gray. The calculated thyroid dose of a person i is expressed as Dimes=fiQi(mes)/Mi(mes). Here, Qi(mes) is the measured content of radioiodine in the thyroid gland of person i at time t(mes), Mi(mes) is the estimate of the thyroid mass, and f(i) is the normalizing multiplier. The Q(i) and M(i) are measured with multiplicative errors Vi(Q) and ViM, so that Qi(mes)=Qi(tr)Vi(Q) (this is classical measurement error model) and Mi(tr)=Mi(mes)Vi(M) (this is Berkson measurement error model). Here, Qi(tr) is the true content of radioactivity in the thyroid gland, and Mi(tr) is the true value of the thyroid mass. The error in f(i) is much smaller than the errors in ( Qi(mes), Mi(mes)) and ignored in the analysis. By means of Parametric Full Maximum Likelihood and Regression Calibration (under the assumption that the data set of true doses has lognormal distribution), Nonparametric Full Maximum Likelihood, Nonparametric Regression Calibration, and by properly tuned SIMEX method we study the influence of measurement errors in thyroid dose on the estimates of λ(0) and EAR. The simulation study is presented based on a real sample from the epidemiological studies. The doses were reconstructed in the framework of the Ukrainian-American project on the investigation of Post-Chernobyl thyroid cancers in Ukraine, and the underlying subpolulation was artificially enlarged in order to increase the statistical power. The true risk parameters were given by the values to earlier epidemiological studies, and then the binary response was simulated according to the dose-response model.

Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy.

PubMed

Lorenzen, Ebbe L; Brink, Carsten; Taylor, Carolyn W; Darby, Sarah C; Ewertz, Marianne

2016-04-01

We evaluated the accuracy of three methods of estimating radiation dose to the heart from two-dimensional tangential radiotherapy for breast cancer, as used in Denmark during 1982-2002. Three tangential radiotherapy regimens were reconstructed using CT-based planning scans for 40 patients with left-sided and 10 with right-sided breast cancer. Setup errors and organ motion were simulated using estimated uncertainties. For left-sided patients, mean heart dose was related to maximum heart distance in the medial field. For left-sided breast cancer, mean heart dose estimated from individual CT-scans varied from <1Gy to >8Gy, and maximum dose from 5 to 50Gy for all three regimens, so that estimates based only on regimen had substantial uncertainty. When maximum heart distance was taken into account, the uncertainty was reduced and was comparable to the uncertainty of estimates based on individual CT-scans. For right-sided breast cancer patients, mean heart dose based on individual CT-scans was always <1Gy and maximum dose always <5Gy for all three regimens. The use of stored individual simulator films provides a method for estimating heart doses in left-tangential radiotherapy for breast cancer that is almost as accurate as estimates based on individual CT-scans. Copyright © 2016. Published by Elsevier Ireland Ltd.

Estimating missing daily temperature extremes in Jaffna, Sri Lanka

NASA Astrophysics Data System (ADS)

Thevakaran, A.; Sonnadara, D. U. J.

2018-04-01

The accuracy of reconstructing missing daily temperature extremes in the Jaffna climatological station, situated in the northern part of the dry zone of Sri Lanka, is presented. The adopted method utilizes standard departures of daily maximum and minimum temperature values at four neighbouring stations, Mannar, Anuradhapura, Puttalam and Trincomalee to estimate the standard departures of daily maximum and minimum temperatures at the target station, Jaffna. The daily maximum and minimum temperatures from 1966 to 1980 (15 years) were used to test the validity of the method. The accuracy of the estimation is higher for daily maximum temperature compared to daily minimum temperature. About 95% of the estimated daily maximum temperatures are within ±1.5 °C of the observed values. For daily minimum temperature, the percentage is about 92. By calculating the standard deviation of the difference in estimated and observed values, we have shown that the error in estimating the daily maximum and minimum temperatures is ±0.7 and ±0.9 °C, respectively. To obtain the best accuracy when estimating the missing daily temperature extremes, it is important to include Mannar which is the nearest station to the target station, Jaffna. We conclude from the analysis that the method can be applied successfully to reconstruct the missing daily temperature extremes in Jaffna where no data is available due to frequent disruptions caused by civil unrests and hostilities in the region during the period, 1984 to 2000.

Influence of erroneous patient records on population pharmacokinetic modeling and individual bayesian estimation.

PubMed

van der Meer, Aize Franciscus; Touw, Daniël J; Marcus, Marco A E; Neef, Cornelis; Proost, Johannes H

2012-10-01

Observational data sets can be used for population pharmacokinetic (PK) modeling. However, these data sets are generally less precisely recorded than experimental data sets. This article aims to investigate the influence of erroneous records on population PK modeling and individual maximum a posteriori Bayesian (MAPB) estimation. A total of 1123 patient records of neonates who were administered vancomycin were used for population PK modeling by iterative 2-stage Bayesian (ITSB) analysis. Cut-off values for weighted residuals were tested for exclusion of records from the analysis. A simulation study was performed to assess the influence of erroneous records on population modeling and individual MAPB estimation. Also the cut-off values for weighted residuals were tested in the simulation study. Errors in registration have limited the influence on outcomes of population PK modeling but can have detrimental effects on individual MAPB estimation. A population PK model created from a data set with many registration errors has little influence on subsequent MAPB estimates for precisely recorded data. A weighted residual value of 2 for concentration measurements has good discriminative power for identification of erroneous records. ITSB analysis and its individual estimates are hardly affected by most registration errors. Large registration errors can be detected by weighted residuals of concentration.

Parameter Estimation for Thurstone Choice Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vojnovic, Milan; Yun, Seyoung

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one ormore » more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.« less

Equalization of nonlinear transmission impairments by maximum-likelihood-sequence estimation in digital coherent receivers.

PubMed

Khairuzzaman, Md; Zhang, Chao; Igarashi, Koji; Katoh, Kazuhiro; Kikuchi, Kazuro

2010-03-01

We describe a successful introduction of maximum-likelihood-sequence estimation (MLSE) into digital coherent receivers together with finite-impulse response (FIR) filters in order to equalize both linear and nonlinear fiber impairments. The MLSE equalizer based on the Viterbi algorithm is implemented in the offline digital signal processing (DSP) core. We transmit 20-Gbit/s quadrature phase-shift keying (QPSK) signals through a 200-km-long standard single-mode fiber. The bit-error rate performance shows that the MLSE equalizer outperforms the conventional adaptive FIR filter, especially when nonlinear impairments are predominant.

Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm

NASA Astrophysics Data System (ADS)

Gilat-Schmidt, Taly; Wang, Adam; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-03-01

The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

Vertical Jump Height Estimation Algorithm Based on Takeoff and Landing Identification Via Foot-Worn Inertial Sensing.

PubMed

Wang, Jianren; Xu, Junkai; Shull, Peter B

2018-03-01

Vertical jump height is widely used for assessing motor development, functional ability, and motor capacity. Traditional methods for estimating vertical jump height rely on force plates or optical marker-based motion capture systems limiting assessment to people with access to specialized laboratories. Current wearable designs need to be attached to the skin or strapped to an appendage which can potentially be uncomfortable and inconvenient to use. This paper presents a novel algorithm for estimating vertical jump height based on foot-worn inertial sensors. Twenty healthy subjects performed countermovement jumping trials and maximum jump height was determined via inertial sensors located above the toe and under the heel and was compared with the gold standard maximum jump height estimation via optical marker-based motion capture. Average vertical jump height estimation errors from inertial sensing at the toe and heel were -2.2±2.1 cm and -0.4±3.8 cm, respectively. Vertical jump height estimation with the presented algorithm via inertial sensing showed excellent reliability at the toe (ICC(2,1)=0.98) and heel (ICC(2,1)=0.97). There was no significant bias in the inertial sensing at the toe, but proportional bias (b=1.22) and fixed bias (a=-10.23cm) were detected in inertial sensing at the heel. These results indicate that the presented algorithm could be applied to foot-worn inertial sensors to estimate maximum jump height enabling assessment outside of traditional laboratory settings, and to avoid bias errors, the toe may be a more suitable location for inertial sensor placement than the heel.

The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling under Conditions of Misspecification and Nonnormality.

ERIC Educational Resources Information Center

Olsson, Ulf Henning; Foss, Tron; Troye, Sigurd V.; Howell, Roy D.

2000-01-01

Used simulation to demonstrate how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Discusses results for maximum likelihood (ML), generalized least squares (GLS), and weighted least…

A soft-computing methodology for noninvasive time-spatial temperature estimation.

PubMed

Teixeira, César A; Ruano, Maria Graça; Ruano, António E; Pereira, Wagner C A

2008-02-01

The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 degrees C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 degrees C, being "elected" as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Cost effectiveness of the US Geological Survey stream-gaging program in Alabama

USGS Publications Warehouse

Jeffcoat, H.H.

1987-01-01

A study of the cost effectiveness of the stream gaging program in Alabama identified data uses and funding sources for 72 surface water stations (including dam stations, slope stations, and continuous-velocity stations) operated by the U.S. Geological Survey in Alabama with a budget of $393,600. Of these , 58 gaging stations were used in all phases of the analysis at a funding level of $328,380. For the current policy of operation of the 58-station program, the average standard error of estimation of instantaneous discharge is 29.3%. This overall level of accuracy can be maintained with a budget of $319,800 by optimizing routes and implementing some policy changes. The maximum budget considered in the analysis was $361,200, which gave an average standard error of estimation of 20.6%. The minimum budget considered was $299,360, with an average standard error of estimation of 36.5%. The study indicates that a major source of error in the stream gaging records is lost or missing data that are the result of streamside equipment failure. If perfect equipment were available, the standard error in estimating instantaneous discharge under the current program and budget could be reduced to 18.6%. This can also be interpreted to mean that the streamflow data records have a standard error of this magnitude during times when the equipment is operating properly. (Author 's abstract)

The moving-window Bayesian maximum entropy framework: estimation of PM(2.5) yearly average concentration across the contiguous United States.

PubMed

Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L

2012-09-01

Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM(2.5). Moreover, the MWBME method further reduces the MSE by 8.4-43.7%, with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM(2.5) across large geographical domains with expected spatial non-stationarity.

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

PubMed

Hedeker, D; Gibbons, R D

1996-05-01

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks

PubMed Central

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X.; Li, Huailiang; Shi, Rui

2017-01-01

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring. PMID:28471418

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks.

PubMed

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X; Li, Huailiang; Shi, Rui

2017-05-04

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1981-01-01

Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Analysis of the Magnitude and Frequency of Peak Discharge and Maximum Observed Peak Discharge in New Mexico and Surrounding Areas

USGS Publications Warehouse

Waltemeyer, Scott D.

2008-01-01

Estimates of the magnitude and frequency of peak discharges are necessary for the reliable design of bridges, culverts, and open-channel hydraulic analysis, and for flood-hazard mapping in New Mexico and surrounding areas. The U.S. Geological Survey, in cooperation with the New Mexico Department of Transportation, updated estimates of peak-discharge magnitude for gaging stations in the region and updated regional equations for estimation of peak discharge and frequency at ungaged sites. Equations were developed for estimating the magnitude of peak discharges for recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years at ungaged sites by use of data collected through 2004 for 293 gaging stations on unregulated streams that have 10 or more years of record. Peak discharges for selected recurrence intervals were determined at gaging stations by fitting observed data to a log-Pearson Type III distribution with adjustments for a low-discharge threshold and a zero skew coefficient. A low-discharge threshold was applied to frequency analysis of 140 of the 293 gaging stations. This application provides an improved fit of the log-Pearson Type III frequency distribution. Use of the low-discharge threshold generally eliminated the peak discharge by having a recurrence interval of less than 1.4 years in the probability-density function. Within each of the nine regions, logarithms of the maximum peak discharges for selected recurrence intervals were related to logarithms of basin and climatic characteristics by using stepwise ordinary least-squares regression techniques for exploratory data analysis. Generalized least-squares regression techniques, an improved regression procedure that accounts for time and spatial sampling errors, then were applied to the same data used in the ordinary least-squares regression analyses. The average standard error of prediction, which includes average sampling error and average standard error of regression, ranged from 38 to 93 percent (mean value is 62, and median value is 59) for the 100-year flood. The 1996 investigation standard error of prediction for the flood regions ranged from 41 to 96 percent (mean value is 67, and median value is 68) for the 100-year flood that was analyzed by using generalized least-squares regression analysis. Overall, the equations based on generalized least-squares regression techniques are more reliable than those in the 1996 report because of the increased length of record and improved geographic information system (GIS) method to determine basin and climatic characteristics. Flood-frequency estimates can be made for ungaged sites upstream or downstream from gaging stations by using a method that transfers flood-frequency data at the gaging station to the ungaged site by using a drainage-area ratio adjustment equation. The peak discharge for a given recurrence interval at the gaging station, drainage-area ratio, and the drainage-area exponent from the regional regression equation of the respective region is used to transfer the peak discharge for the recurrence interval to the ungaged site. Maximum observed peak discharge as related to drainage area was determined for New Mexico. Extreme events are commonly used in the design and appraisal of bridge crossings and other structures. Bridge-scour evaluations are commonly made by using the 500-year peak discharge for these appraisals. Peak-discharge data collected at 293 gaging stations and 367 miscellaneous sites were used to develop a maximum peak-discharge relation as an alternative method of estimating peak discharge of an extreme event such as a maximum probable flood.

A Measurement of Gravitational Lensing of the Cosmic Microwave Background by Galaxy Clusters Using Data from the South Pole Telescope

DOE PAGES

Baxter, E. J.; Keisler, R.; Dodelson, S.; ...

2015-06-22

Clusters of galaxies are expected to gravitationally lens the cosmic microwave background (CMB) and thereby generate a distinct signal in the CMB on arcminute scales. Measurements of this effect can be used to constrain the masses of galaxy clusters with CMB data alone. Here we present a measurement of lensing of the CMB by galaxy clusters using data from the South Pole Telescope (SPT). We also develop a maximum likelihood approach to extract the CMB cluster lensing signal and validate the method on mock data. We quantify the effects on our analysis of several potential sources of systematic error andmore » find that they generally act to reduce the best-fit cluster mass. It is estimated that this bias to lower cluster mass is roughly 0.85σ in units of the statistical error bar, although this estimate should be viewed as an upper limit. Furthermore, we apply our maximum likelihood technique to 513 clusters selected via their Sunyaev–Zeldovich (SZ) signatures in SPT data, and rule out the null hypothesis of no lensing at 3.1σ. The lensing-derived mass estimate for the full cluster sample is consistent with that inferred from the SZ flux: M 200,lens = 0.83 +0.38 -0.37 M 200,SZ (68% C.L., statistical error only).« less

Guaranteed convergence of the Hough transform

NASA Astrophysics Data System (ADS)

Soffer, Menashe; Kiryati, Nahum

1995-01-01

The straight-line Hough Transform using normal parameterization with a continuous voting kernel is considered. It transforms the colinearity detection problem to a problem of finding the global maximum of a two dimensional function above a domain in the parameter space. The principle is similar to robust regression using fixed scale M-estimation. Unlike standard M-estimation procedures the Hough Transform does not rely on a good initial estimate of the line parameters: The global optimization problem is approached by exhaustive search on a grid that is usually as fine as computationally feasible. The global maximum of a general function above a bounded domain cannot be found by a finite number of function evaluations. Only if sufficient a-priori knowledge about the smoothness of the objective function is available, convergence to the global maximum can be guaranteed. The extraction of a-priori information and its efficient use are the main challenges in real global optimization problems. The global optimization problem in the Hough Transform is essentially how fine should the parameter space quantization be in order not to miss the true maximum. More than thirty years after Hough patented the basic algorithm, the problem is still essentially open. In this paper an attempt is made to identify a-priori information on the smoothness of the objective (Hough) function and to introduce sufficient conditions for the convergence of the Hough Transform to the global maximum. An image model with several application dependent parameters is defined. Edge point location errors as well as background noise are accounted for. Minimal parameter space quantization intervals that guarantee convergence are obtained. Focusing policies for multi-resolution Hough algorithms are developed. Theoretical support for bottom- up processing is provided. Due to the randomness of errors and noise, convergence guarantees are probabilistic.

An affordable cuff-less blood pressure estimation solution.

PubMed

Jain, Monika; Kumar, Niranjan; Deb, Sujay

2016-08-01

This paper presents a cuff-less hypertension pre-screening device that non-invasively monitors the Blood Pressure (BP) and Heart Rate (HR) continuously. The proposed device simultaneously records two clinically significant and highly correlated biomedical signals, viz., Electrocardiogram (ECG) and Photoplethysmogram (PPG). The device provides a common data acquisition platform that can interface with PC/laptop, Smart phone/tablet and Raspberry-pi etc. The hardware stores and processes the recorded ECG and PPG in order to extract the real-time BP and HR using kernel regression approach. The BP and HR estimation error is measured in terms of normalized mean square error, Error Standard Deviation (ESD) and Mean Absolute Error (MAE), with respect to a clinically proven digital BP monitor (OMRON HBP1300). The computed error falls under the maximum standard allowable error mentioned by Association for the Advancement of Medical Instrumentation; MAE <; 5 mmHg and ESD <; 8mmHg. The results are validated using two-tailed dependent sample t-test also. The proposed device is a portable low-cost home and clinic bases solution for continuous health monitoring.

A Global Carbon Assimilation System using a modified EnKF assimilation method

NASA Astrophysics Data System (ADS)

Zhang, S.; Zheng, X.; Chen, Z.; Dan, B.; Chen, J. M.; Yi, X.; Wang, L.; Wu, G.

2014-10-01

A Global Carbon Assimilation System based on Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 abundance data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is based on the ensemble Kalman filter (EnKF), but with several new developments, including using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is tested in observing system simulation experiments and then used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models.

PubMed

Magis, David

2014-11-01

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process. © 2013 The British Psychological Society.

Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches

PubMed Central

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael

2014-01-01

Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566 PMID:24879650

Accuracy of the Heidelberg Spectralis in the alignment between near-infrared image and tomographic scan in a model eye: a multicenter study.

PubMed

Barteselli, Giulio; Bartsch, Dirk-Uwe; Viola, Francesco; Mojana, Francesca; Pellegrini, Marco; Hartmann, Kathrin I; Benatti, Eleonora; Leicht, Simon; Ratiglia, Roberto; Staurenghi, Giovanni; Weinreb, Robert N; Freeman, William R

2013-09-01

To evaluate temporal changes and predictors of accuracy in the alignment between simultaneous near-infrared image and optical coherence tomography (OCT) scan on the Heidelberg Spectralis using a model eye. Laboratory investigation. After calibrating the device, 6 sites performed weekly testing of the alignment for 12 weeks using a model eye. The maximum error was compared with multiple variables to evaluate predictors of inaccurate alignment. Variables included the number of weekly scanned patients, total number of OCT scans and B-scans performed, room temperature and its variation, and working time of the scanning laser. A 4-week extension study was subsequently performed to analyze short-term changes in the alignment. The average maximum error in the alignment was 15 ± 6 μm; the greatest error was 35 μm. The error increased significantly at week 1 (P = .01), specifically after the second imaging study (P < .05); reached a maximum after the eighth patient (P < .001); and then varied randomly over time. Predictors for inaccurate alignment were temperature variation and scans per patient (P < .001). For each 1 unit of increase in temperature variation, the estimated increase in maximum error was 1.26 μm. For the average number of scans per patient, each increase of 1 unit increased the error by 0.34 μm. Overall, the accuracy of the Heidelberg Spectralis was excellent. The greatest error happened in the first week after calibration, and specifically after the second imaging study. To improve the accuracy, room temperature should be kept stable and unnecessary scans should be avoided. The alignment of the device does not need to be checked on a regular basis in the clinical setting, but it should be checked after every other patient for more precise research purposes. Published by Elsevier Inc.

WE-D-BRF-05: Quantitative Dual-Energy CT Imaging for Proton Stopping Power Computation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, D; Williamson, J; Siebers, J

2014-06-15

Purpose: To extend the two-parameter separable basis-vector model (BVM) to estimation of proton stopping power from dual-energy CT (DECT) imaging. Methods: BVM assumes that the photon cross sections of any unknown material can be represented as a linear combination of the corresponding quantities for two bracketing basis materials. We show that both the electron density (ρe) and mean excitation energy (Iex) can be modeled by BVM, enabling stopping power to be estimated from the Bethe-Bloch equation. We have implemented an idealized post-processing dual energy imaging (pDECT) simulation consisting of monogenetic 45 keV and 80 keV scanning beams with polystyrene-water andmore » water-CaCl2 solution basis pairs for soft tissues and bony tissues, respectively. The coefficients of 24 standard ICRU tissue compositions were estimated by pDECT. The corresponding ρe, Iex, and stopping power tables were evaluated via BVM and compared to tabulated ICRU 44 reference values. Results: BVM-based pDECT was found to estimate ρe and Iex with average and maximum errors of 0.5% and 2%, respectively, for the 24 tissues. Proton stopping power values at 175 MeV, show average/maximum errors of 0.8%/1.4%. For adipose, muscle and bone, these errors result range prediction accuracies less than 1%. Conclusion: A new two-parameter separable DECT model (BVM) for estimating proton stopping power was developed. Compared to competing parametric fit DECT models, BVM has the comparable prediction accuracy without necessitating iterative solution of nonlinear equations or a sample-dependent empirical relationship between effective atomic number and Iex. Based on the proton BVM, an efficient iterative statistical DECT reconstruction model is under development.« less

Modelling of extreme rainfall events in Peninsular Malaysia based on annual maximum and partial duration series

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Shinyie, Wendy Ling; Jemain, Abdul Aziz

2015-02-01

In this study, two series of data for extreme rainfall events are generated based on Annual Maximum and Partial Duration Methods, derived from 102 rain-gauge stations in Peninsular from 1982-2012. To determine the optimal threshold for each station, several requirements must be satisfied and Adapted Hill estimator is employed for this purpose. A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. The mean annual frequency is also checked to ensure that it lies in the range of one to five and the resulting data is also de-clustered to ensure independence. The two data series are then fitted to Generalized Extreme Value and Generalized Pareto distributions for annual maximum and partial duration series, respectively. The parameter estimation methods used are the Maximum Likelihood and the L-moment methods. Two goodness of fit tests are then used to evaluate the best-fitted distribution. The results showed that the Partial Duration series with Generalized Pareto distribution and Maximum Likelihood parameter estimation provides the best representation for extreme rainfall events in Peninsular Malaysia for majority of the stations studied. Based on these findings, several return values are also derived and spatial mapping are constructed to identify the distribution characteristic of extreme rainfall in Peninsular Malaysia.

Small Body GN and C Research Report: G-SAMPLE - An In-Flight Dynamical Method for Identifying Sample Mass [External Release Version

NASA Technical Reports Server (NTRS)

Carson, John M., III; Bayard, David S.

2006-01-01

G-SAMPLE is an in-flight dynamical method for use by sample collection missions to identify the presence and quantity of collected sample material. The G-SAMPLE method implements a maximum-likelihood estimator to identify the collected sample mass, based on onboard force sensor measurements, thruster firings, and a dynamics model of the spacecraft. With G-SAMPLE, sample mass identification becomes a computation rather than an extra hardware requirement; the added cost of cameras or other sensors for sample mass detection is avoided. Realistic simulation examples are provided for a spacecraft configuration with a sample collection device mounted on the end of an extended boom. In one representative example, a 1000 gram sample mass is estimated to within 110 grams (95% confidence) under realistic assumptions of thruster profile error, spacecraft parameter uncertainty, and sensor noise. For convenience to future mission design, an overall sample-mass estimation error budget is developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Transmuted of Rayleigh Distribution with Estimation and Application on Noise Signal

NASA Astrophysics Data System (ADS)

Ahmed, Suhad; Qasim, Zainab

2018-05-01

This paper deals with transforming one parameter Rayleigh distribution, into transmuted probability distribution through introducing a new parameter (λ), since this studied distribution is necessary in representing signal data distribution and failure data model the value of this transmuted parameter |λ| ≤ 1, is also estimated as well as the original parameter (⊖) by methods of moments and maximum likelihood using different sample size (n=25, 50, 75, 100) and comparing the results of estimation by statistical measure (mean square error, MSE).

Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy-LUR approaches.

PubMed

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael; Smargiassi, Audrey

2014-09-01

Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.

Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: statistical and systematic error budgets for future experiments

NASA Astrophysics Data System (ADS)

Raghunathan, Srinivasan; Patil, Sanjaykumar; Baxter, Eric J.; Bianchini, Federico; Bleem, Lindsey E.; Crawford, Thomas M.; Holder, Gilbert P.; Manzotti, Alessandro; Reichardt, Christian L.

2017-08-01

We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at low noise levels in temperature, this optimal estimator outperforms the standard quadratic estimator by a factor of two. For polarization, we show that the Stokes Q/U maps can be used instead of the traditional E- and B-mode maps without losing information. We test and quantify the bias in the recovered lensing mass for a comprehensive list of potential systematic errors. Using realistic simulations, we examine the cluster mass uncertainties from CMB-cluster lensing as a function of an experiment's beam size and noise level. We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT, and Simons Array experiments with 10,000 clusters and less than 1% for the CMB-S4 experiment with a sample containing 100,000 clusters. The mass constraints from CMB polarization are very sensitive to the experimental beam size and map noise level: for a factor of three reduction in either the beam size or noise level, the lensing signal-to-noise improves by roughly a factor of two.

Maximum-Likelihood Estimation for Frequency-Modulated Continuous-Wave Laser Ranging Using Photon-Counting Detectors

DTIC Science & Technology

2013-01-01

are calculated from coherently -detected fields, e.g., coherent Doppler lidar . Our CRB results reveal that the best-case mean-square error scales as 1...1088 (2001). 7. K. Asaka, Y. Hirano, K. Tatsumi, K. Kasahara, and T. Tajime, “A pseudo-random frequency modulation continuous wave coherent lidar using...multiple returns,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 2170–2180 (2007). 11. T. J. Karr, “Atmospheric phase error in coherent laser radar

Estimation of distributional parameters for censored trace level water quality data: 1. Estimation techniques

USGS Publications Warehouse

Gilliom, Robert J.; Helsel, Dennis R.

1986-01-01

A recurring difficulty encountered in investigations of many metals and organic contaminants in ambient waters is that a substantial portion of water sample concentrations are below limits of detection established by analytical laboratories. Several methods were evaluated for estimating distributional parameters for such censored data sets using only uncensored observations. Their reliabilities were evaluated by a Monte Carlo experiment in which small samples were generated from a wide range of parent distributions and censored at varying levels. Eight methods were used to estimate the mean, standard deviation, median, and interquartile range. Criteria were developed, based on the distribution of uncensored observations, for determining the best performing parameter estimation method for any particular data set. The most robust method for minimizing error in censored-sample estimates of the four distributional parameters over all simulation conditions was the log-probability regression method. With this method, censored observations are assumed to follow the zero-to-censoring level portion of a lognormal distribution obtained by a least squares regression between logarithms of uncensored concentration observations and their z scores. When method performance was separately evaluated for each distributional parameter over all simulation conditions, the log-probability regression method still had the smallest errors for the mean and standard deviation, but the lognormal maximum likelihood method had the smallest errors for the median and interquartile range. When data sets were classified prior to parameter estimation into groups reflecting their probable parent distributions, the ranking of estimation methods was similar, but the accuracy of error estimates was markedly improved over those without classification.

Estimation of distributional parameters for censored trace level water quality data. 1. Estimation Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilliom, R.J.; Helsel, D.R.

1986-02-01

A recurring difficulty encountered in investigations of many metals and organic contaminants in ambient waters is that a substantial portion of water sample concentrations are below limits of detection established by analytical laboratories. Several methods were evaluated for estimating distributional parameters for such censored data sets using only uncensored observations. Their reliabilities were evaluated by a Monte Carlo experiment in which small samples were generated from a wide range of parent distributions and censored at varying levels. Eight methods were used to estimate the mean, standard deviation, median, and interquartile range. Criteria were developed, based on the distribution of uncensoredmore » observations, for determining the best performing parameter estimation method for any particular data det. The most robust method for minimizing error in censored-sample estimates of the four distributional parameters over all simulation conditions was the log-probability regression method. With this method, censored observations are assumed to follow the zero-to-censoring level portion of a lognormal distribution obtained by a least squares regression between logarithms of uncensored concentration observations and their z scores. When method performance was separately evaluated for each distributional parameter over all simulation conditions, the log-probability regression method still had the smallest errors for the mean and standard deviation, but the lognormal maximum likelihood method had the smallest errors for the median and interquartile range. When data sets were classified prior to parameter estimation into groups reflecting their probable parent distributions, the ranking of estimation methods was similar, but the accuracy of error estimates was markedly improved over those without classification.« less

Errors in the estimation method for the rejection of vibrations in adaptive optics systems

NASA Astrophysics Data System (ADS)

Kania, Dariusz

2017-06-01

In recent years the problem of the mechanical vibrations impact in adaptive optics (AO) systems has been renewed. These signals are damped sinusoidal signals and have deleterious effect on the system. One of software solutions to reject the vibrations is an adaptive method called AVC (Adaptive Vibration Cancellation) where the procedure has three steps: estimation of perturbation parameters, estimation of the frequency response of the plant, update the reference signal to reject/minimalize the vibration. In the first step a very important problem is the estimation method. A very accurate and fast (below 10 ms) estimation method of these three parameters has been presented in several publications in recent years. The method is based on using the spectrum interpolation and MSD time windows and it can be used to estimate multifrequency signals. In this paper the estimation method is used in the AVC method to increase the system performance. There are several parameters that affect the accuracy of obtained results, e.g. CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, b - number of ADC bits, γ - damping ratio of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value for systematic error is approximately 10^-10 Hz/Hz for N = 2048 and CiR = 0.1. This paper presents equations that can used to estimate maximum systematic errors for given values of H, CiR and N before the start of the estimation process.

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV-infected infants and toddlers

PubMed Central

Zhao, Wei; Cella, Massimo; Della Pasqua, Oscar; Burger, David; Jacqz-Aigrain, Evelyne

2012-01-01

AIMS To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration–time curve (AUC) targeted dosage and individualize therapy. METHODS The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation–estimation method. RESULTS The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 l h−1 (RSE 6.3%), apparent central volume of distribution 4.94 l (RSE 28.7%), apparent peripheral volume of distribution 8.12 l (RSE14.2%), apparent intercompartment clearance 1.25 l h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t. CONCLUSIONS The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC0–t was developed from the final model and can be used routinely to optimize individual dosing. PMID:21988586

The moving-window Bayesian Maximum Entropy framework: Estimation of PM2.5 yearly average concentration across the contiguous United States

PubMed Central

Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L.

2013-01-01

Geostatistical methods are widely used in estimating long-term exposures for air pollution epidemiological studies, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian Maximum Entropy (BME) method and applied this framework to estimate fine particulate matter (PM2.5) yearly average concentrations over the contiguous U.S. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingnees in the air monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM2.5 data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM2.5. Moreover, the MWBME method further reduces the MSE by 8.4% to 43.7% with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM2.5 across large geographical domains with expected spatial non-stationarity. PMID:22739679

Evaluation of methods for calculating maximum allowable standing height in amputees competing in Paralympic athletics.

PubMed

Connick, M J; Beckman, E; Ibusuki, T; Malone, L; Tweedy, S M

2016-11-01

The International Paralympic Committee has a maximum allowable standing height (MASH) rule that limits stature to a pre-trauma estimation. The MASH rule reduces the probability that bilateral lower limb amputees use disproportionately long prostheses in competition. Although there are several methods for estimating stature, the validity of these methods has not been compared. To identify the most appropriate method for the MASH rule, this study aimed to compare the criterion validity of estimations resulting from the current method, the Contini method, and four Canda methods (Canda-1, Canda-2, Canda-3, and Canda-4). Stature, ulna length, demispan, sitting height, thigh length, upper arm length, and forearm length measurements in 31 males and 30 females were used to calculate the respective estimation for each method. Results showed that Canda-1 (based on four anthropometric variables) produced the smallest error and best fitted the data in males and females. The current method was associated with the largest error of those tests because it increasingly overestimated height in people with smaller stature. The results suggest that the set of Canda equations provide a more valid MASH estimation in people with a range of upper limb and bilateral lower limb amputations compared with the current method. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Best estimate of luminal cross-sectional area of coronary arteries from angiograms

NASA Technical Reports Server (NTRS)

Lee, P. L.; Selzer, R. H.

1988-01-01

We have reexamined the problem of estimating the luminal area of an elliptically-shaped coronary artery cross section from two or more radiographic diameter measurements. The expected error is found to be much smaller than the maximum potential error. In the cae of two orthogonal views, closed form expressions have been derived for calculating the area and the uncertainty. Assuming that the underlying ellipse has limited ellipticity (major/minor axis ratio less than five), it is shown that the average uncertainty in the area is less than 14 percent. When more than two views are available, we suggest using a least-squares fit method to extract all available information from the data.

Stereo pair design for cameras with a fovea

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Keefe, Michael; Zimmerman, John R.

1992-01-01

We describe the methodology for the design and selection of a stereo pair when the cameras have a greater concentration of sensing elements in the center of the image plane (fovea). Binocular vision is important for the purpose of depth estimation, which in turn is important in a variety of applications such as gaging and autonomous vehicle guidance. We assume that one camera has square pixels of size dv and the other has pixels of size rdv, where r is between 0 and 1. We then derive results for the average error, the maximum error, and the error distribution in the depth determination of a point. These results can be shown to be a general form of the results for the case when the cameras have equal sized pixels. We discuss the behavior of the depth estimation error as we vary r and the tradeoffs between the extra processing time and increased accuracy. Knowing these results makes it possible to study the case when we have a pair of cameras with a fovea.

Impact of sampling strategy on stream load estimates in till landscape of the Midwest

USGS Publications Warehouse

Vidon, P.; Hubbard, L.E.; Soyeux, E.

2009-01-01

Accurately estimating various solute loads in streams during storms is critical to accurately determine maximum daily loads for regulatory purposes. This study investigates the impact of sampling strategy on solute load estimates in streams in the US Midwest. Three different solute types (nitrate, magnesium, and dissolved organic carbon (DOC)) and three sampling strategies are assessed. Regardless of the method, the average error on nitrate loads is higher than for magnesium or DOC loads, and all three methods generally underestimate DOC loads and overestimate magnesium loads. Increasing sampling frequency only slightly improves the accuracy of solute load estimates but generally improves the precision of load calculations. This type of investigation is critical for water management and environmental assessment so error on solute load calculations can be taken into account by landscape managers, and sampling strategies optimized as a function of monitoring objectives. ?? 2008 Springer Science+Business Media B.V.

Estimation of reach in peripersonal and extrapersonal space: a developmental view.

PubMed

Gabbard, Carl; Cordova, Alberto; Ammar, Diala

2007-01-01

This study explored the developmental nature of action processing via estimation of reach in peripersonal and extrapersonal space. Children 5 to 11 years of age and adults were tested for estimates of reach to targets presented randomly at seven midline locations. Target distances were scaled to the individual based on absolute maximum reach. While there was no difference between age groups for total error, a significant distinction emerged in reference to space. With children, significantly more error was exhibited in extrapersonal space; no difference was found with adults. The groups did not differ in peripersonal space; however, adults were substantially more accurate with extrapersonal targets. Furthermore, children displayed a greater tendency to overestimate. In essence, these data reveal a body-scaling problem in children in estimating reach in extrapersonal space. Future work should focus on possible developmental differences in use of visual information and state of confidence.

Simple Form of MMSE Estimator for Super-Gaussian Prior Densities

NASA Astrophysics Data System (ADS)

Kittisuwan, Pichid

2015-04-01

The denoising method that become popular in recent years for additive white Gaussian noise (AWGN) are Bayesian estimation techniques e.g., maximum a posteriori (MAP) and minimum mean square error (MMSE). In super-Gaussian prior densities, it is well known that the MMSE estimator in such a case has a complicated form. In this work, we derive the MMSE estimation with Taylor series. We show that the proposed estimator also leads to a simple formula. An extension of this estimator to Pearson type VII prior density is also offered. The experimental result shows that the proposed estimator to the original MMSE nonlinearity is reasonably good.

Maximum sustainable yield estimates of Ladypees, Sillago sihama (Forsskål), fishery in Pakistan using the ASPIC and CEDA packages

NASA Astrophysics Data System (ADS)

Panhwar, Sher Khan; Liu, Qun; Khan, Fozia; Siddiqui, Pirzada J. A.

2012-03-01

Using surplus production model packages of ASPIC (a stock-production model incorporating covariates) and CEDA (Catch effort data analysis), we analyzed the catch and effort data of Sillago sihama fishery in Pakistan. ASPIC estimates the parameters of MSY (maximum sustainable yield), F msy (fishing mortality), q (catchability coefficient), K (carrying capacity or unexploited biomass) and B1/K (maximum sustainable yield over initial biomass). The estimated non-bootstrapped value of MSY based on logistic was 598 t and that based on the Fox model was 415 t, which showed that the Fox model estimation was more conservative than that with the logistic model. The R 2 with the logistic model (0.702) is larger than that with the Fox model (0.541), which indicates a better fit. The coefficient of variation (cv) of the estimated MSY was about 0.3, except for a larger value 88.87 and a smaller value of 0.173. In contrast to the ASPIC results, the R 2 with the Fox model (0.651-0.692) was larger than that with the Schaefer model (0.435-0.567), indicating a better fit. The key parameters of CEDA are: MSY, K, q, and r (intrinsic growth), and the three error assumptions in using the models are normal, log normal and gamma. Parameter estimates from the Schaefer and Pella-Tomlinson models were similar. The MSY estimations from the above two models were 398 t, 549 t and 398 t for normal, log-normal and gamma error distributions, respectively. The MSY estimates from the Fox model were 381 t, 366 t and 366 t for the above three error assumptions, respectively. The Fox model estimates were smaller than those for the Schaefer and the Pella-Tomlinson models. In the light of the MSY estimations of 415 t from ASPIC for the Fox model and 381 t from CEDA for the Fox model, MSY for S. sihama is about 400 t. As the catch in 2003 was 401 t, we would suggest the fishery should be kept at the current level. Production models used here depend on the assumption that CPUE (catch per unit effort) data used in the study can reliably quantify temporal variability in population abundance, hence the modeling results would be wrong if such an assumption is not met. Because the reliability of this CPUE data in indexing fish population abundance is unknown, we should be cautious with the interpretation and use of the derived population and management parameters.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

2013-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R.M. 2007. Robust estimation of the variogram by residual maximum likelihood. Geoderma 140: 62-72. Richardson, A.M. and Welsh, A.H. 1995. Robust restricted maximum likelihood in mixed linear models. Biometrics 51: 1429-1439. Welsh, A.H. and Richardson, A.M. 1997. Approaches to the robust estimation of mixed models. In: Handbook of Statistics Vol. 15, Elsevier, pp. 343-384.

Detecting and quantifying stellar magnetic fields. Sparse Stokes profile approximation using orthogonal matching pursuit

NASA Astrophysics Data System (ADS)

Carroll, T. A.; Strassmeier, K. G.

2014-03-01

Context. In recent years, we have seen a rapidly growing number of stellar magnetic field detections for various types of stars. Many of these magnetic fields are estimated from spectropolarimetric observations (Stokes V) by using the so-called center-of-gravity (COG) method. Unfortunately, the accuracy of this method rapidly deteriorates with increasing noise and thus calls for a more robust procedure that combines signal detection and field estimation. Aims: We introduce an estimation method that provides not only the effective or mean longitudinal magnetic field from an observed Stokes V profile but also uses the net absolute polarization of the profile to obtain an estimate of the apparent (i.e., velocity resolved) absolute longitudinal magnetic field. Methods: By combining the COG method with an orthogonal-matching-pursuit (OMP) approach, we were able to decompose observed Stokes profiles with an overcomplete dictionary of wavelet-basis functions to reliably reconstruct the observed Stokes profiles in the presence of noise. The elementary wave functions of the sparse reconstruction process were utilized to estimate the effective longitudinal magnetic field and the apparent absolute longitudinal magnetic field. A multiresolution analysis complements the OMP algorithm to provide a robust detection and estimation method. Results: An extensive Monte-Carlo simulation confirms the reliability and accuracy of the magnetic OMP approach where a mean error of under 2% is found. Its full potential is obtained for heavily noise-corrupted Stokes profiles with signal-to-noise variance ratios down to unity. In this case a conventional COG method yields a mean error for the effective longitudinal magnetic field of up to 50%, whereas the OMP method gives a maximum error of 18%. It is, moreover, shown that even in the case of very small residual noise on a level between 10-3 and 10-5, a regime reached by current multiline reconstruction techniques, the conventional COG method incorrectly interprets a large portion of the residual noise as a magnetic field, with values of up to 100 G. The magnetic OMP method, on the other hand, remains largely unaffected by the noise, regardless of the noise level the maximum error is no greater than 0.7 G.

Selecting SNPs informative for African, American Indian and European Ancestry: application to the Family Investigation of Nephropathy and Diabetes (FIND).

PubMed

Williams, Robert C; Elston, Robert C; Kumar, Pankaj; Knowler, William C; Abboud, Hanna E; Adler, Sharon; Bowden, Donald W; Divers, Jasmin; Freedman, Barry I; Igo, Robert P; Ipp, Eli; Iyengar, Sudha K; Kimmel, Paul L; Klag, Michael J; Kohn, Orly; Langefeld, Carl D; Leehey, David J; Nelson, Robert G; Nicholas, Susanne B; Pahl, Madeleine V; Parekh, Rulan S; Rotter, Jerome I; Schelling, Jeffrey R; Sedor, John R; Shah, Vallabh O; Smith, Michael W; Taylor, Kent D; Thameem, Farook; Thornley-Brown, Denyse; Winkler, Cheryl A; Guo, Xiuqing; Zager, Phillip; Hanson, Robert L

2016-05-04

The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Rib biomechanical properties exhibit diagnostic potential for accurate ageing in forensic investigations

PubMed Central

Bonicelli, Andrea; Xhemali, Bledar; Kranioti, Elena F.

2017-01-01

Age estimation remains one of the most challenging tasks in forensic practice when establishing a biological profile of unknown skeletonised remains. Morphological methods based on developmental markers of bones can provide accurate age estimates at a young age, but become highly unreliable for ages over 35 when all developmental markers disappear. This study explores the changes in the biomechanical properties of bone tissue and matrix, which continue to change with age even after skeletal maturity, and their potential value for age estimation. As a proof of concept we investigated the relationship of 28 variables at the macroscopic and microscopic level in rib autopsy samples from 24 individuals. Stepwise regression analysis produced a number of equations one of which with seven variables showed an R2 = 0.949; a mean residual error of 2.13 yrs ±0.4 (SD) and a maximum residual error value of 2.88 yrs. For forensic purposes, by using only bench top machines in tests which can be carried out within 36 hrs, a set of just 3 variables produced an equation with an R2 = 0.902 a mean residual error of 3.38 yrs ±2.6 (SD) and a maximum observed residual error 9.26yrs. This method outstrips all existing age-at-death methods based on ribs, thus providing a novel lab based accurate tool in the forensic investigation of human remains. The present application is optimised for fresh (uncompromised by taphonomic conditions) remains, but the potential of the principle and method is vast once the trends of the biomechanical variables are established for other environmental conditions and circumstances. PMID:28520764

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

NASA Technical Reports Server (NTRS)

Tsaoussi, Lucia S.; Koblinsky, Chester J.

1994-01-01

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Maximum entropy approach to statistical inference for an ocean acoustic waveguide.

PubMed

Knobles, D P; Sagers, J D; Koch, R A

2012-02-01

A conditional probability distribution suitable for estimating the statistical properties of ocean seabed parameter values inferred from acoustic measurements is derived from a maximum entropy principle. The specification of the expectation value for an error function constrains the maximization of an entropy functional. This constraint determines the sensitivity factor (β) to the error function of the resulting probability distribution, which is a canonical form that provides a conservative estimate of the uncertainty of the parameter values. From the conditional distribution, marginal distributions for individual parameters can be determined from integration over the other parameters. The approach is an alternative to obtaining the posterior probability distribution without an intermediary determination of the likelihood function followed by an application of Bayes' rule. In this paper the expectation value that specifies the constraint is determined from the values of the error function for the model solutions obtained from a sparse number of data samples. The method is applied to ocean acoustic measurements taken on the New Jersey continental shelf. The marginal probability distribution for the values of the sound speed ratio at the surface of the seabed and the source levels of a towed source are examined for different geoacoustic model representations. © 2012 Acoustical Society of America

Statistical approaches to account for false-positive errors in environmental DNA samples.

PubMed

Lahoz-Monfort, José J; Guillera-Arroita, Gurutzeta; Tingley, Reid

2016-05-01

Environmental DNA (eDNA) sampling is prone to both false-positive and false-negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false-positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false-positive rates. We advocate alternative approaches to account for false-positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false-positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false-negative and false-positive errors, the methods presented here should be more routinely adopted in eDNA studies. © 2015 John Wiley & Sons Ltd.

Estimation of distributional parameters for censored trace-level water-quality data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilliom, R.J.; Helsel, D.R.

1984-01-01

A recurring difficulty encountered in investigations of many metals and organic contaminants in ambient waters is that a substantial portion of water-sample concentrations are below limits of detection established by analytical laboratories. Several methods were evaluated for estimating distributional parameters for such censored data sets using only uncensored observations. Their reliabilities were evaluated by a Monte Carlo experiment in which small samples were generated from a wide range of parent distributions and censored at varying levels. Eight methods were used to estimate the mean, standard deviation, median, and interquartile range. Criteria were developed, based on the distribution of uncensored observations,more » for determining the best-performing parameter estimation method for any particular data set. The most robust method for minimizing error in censored-sample estimates of the four distributional parameters over all simulation conditions was the log-probability regression method. With this method, censored observations are assumed to follow the zero-to-censoring level portion of a lognormal distribution obtained by a least-squares regression between logarithms of uncensored concentration observations and their z scores. When method performance was separately evaluated for each distributional parameter over all simulation conditions, the log-probability regression method still had the smallest errors for the mean and standard deviation, but the lognormal maximum likelihood method had the smallest errors for the median and interquartile range. When data sets were classified prior to parameter estimation into groups reflecting their probable parent distributions, the ranking of estimation methods was similar, but the accuracy of error estimates was markedly improved over those without classification. 6 figs., 6 tabs.« less

Error analysis of speed of sound reconstruction in ultrasound limited angle transmission tomography.

PubMed

Jintamethasawat, Rungroj; Lee, Won-Mean; Carson, Paul L; Hooi, Fong Ming; Fowlkes, J Brian; Goodsitt, Mitchell M; Sampson, Richard; Wenisch, Thomas F; Wei, Siyuan; Zhou, Jian; Chakrabarti, Chaitali; Kripfgans, Oliver D

2018-04-07

We have investigated limited angle transmission tomography to estimate speed of sound (SOS) distributions for breast cancer detection. That requires both accurate delineations of major tissues, in this case by segmentation of prior B-mode images, and calibration of the relative positions of the opposed transducers. Experimental sensitivity evaluation of the reconstructions with respect to segmentation and calibration errors is difficult with our current system. Therefore, parametric studies of SOS errors in our bent-ray reconstructions were simulated. They included mis-segmentation of an object of interest or a nearby object, and miscalibration of relative transducer positions in 3D. Close correspondence of reconstruction accuracy was verified in the simplest case, a cylindrical object in homogeneous background with induced segmentation and calibration inaccuracies. Simulated mis-segmentation in object size and lateral location produced maximum SOS errors of 6.3% within 10 mm diameter change and 9.1% within 5 mm shift, respectively. Modest errors in assumed transducer separation produced the maximum SOS error from miscalibrations (57.3% within 5 mm shift), still, correction of this type of error can easily be achieved in the clinic. This study should aid in designing adequate transducer mounts and calibration procedures, and in specification of B-mode image quality and segmentation algorithms for limited angle transmission tomography relying on ray tracing algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimating extreme stream temperatures by the standard deviate method

NASA Astrophysics Data System (ADS)

Bogan, Travis; Othmer, Jonathan; Mohseni, Omid; Stefan, Heinz

2006-02-01

It is now widely accepted that global climate warming is taking place on the earth. Among many other effects, a rise in air temperatures is expected to increase stream temperatures indefinitely. However, due to evaporative cooling, stream temperatures do not increase linearly with increasing air temperatures indefinitely. Within the anticipated bounds of climate warming, extreme stream temperatures may therefore not rise substantially. With this concept in mind, past extreme temperatures measured at 720 USGS stream gauging stations were analyzed by the standard deviate method. In this method the highest stream temperatures are expressed as the mean temperature of a measured partial maximum stream temperature series plus its standard deviation multiplied by a factor KE (standard deviate). Various KE-values were explored; values of KE larger than 8 were found physically unreasonable. It is concluded that the value of KE should be in the range from 7 to 8. A unit error in estimating KE translates into a typical stream temperature error of about 0.5 °C. Using a logistic model for the stream temperature/air temperature relationship, a one degree error in air temperature gives a typical error of 0.16 °C in stream temperature. With a projected error in the enveloping standard deviate dKE=1.0 (range 0.5-1.5) and an error in projected high air temperature d Ta=2 °C (range 0-4 °C), the total projected stream temperature error is estimated as d Ts=0.8 °C.

An analysis of annual maximum streamflows in Terengganu, Malaysia using TL-moments approach

NASA Astrophysics Data System (ADS)

Ahmad, Ummi Nadiah; Shabri, Ani; Zakaria, Zahrahtul Amani

2013-02-01

TL-moments approach has been used in an analysis to determine the best-fitting distributions to represent the annual series of maximum streamflow data over 12 stations in Terengganu, Malaysia. The TL-moments with different trimming values are used to estimate the parameter of the selected distributions namely: generalized pareto (GPA), generalized logistic, and generalized extreme value distribution. The influence of TL-moments on estimated probability distribution functions are examined by evaluating the relative root mean square error and relative bias of quantile estimates through Monte Carlo simulations. The boxplot is used to show the location of the median and the dispersion of the data, which helps in reaching the decisive conclusions. For most of the cases, the results show that TL-moments with one smallest value was trimmed from the conceptual sample (TL-moments (1,0)), of GPA distribution was the most appropriate in majority of the stations for describing the annual maximum streamflow series in Terengganu, Malaysia.

Lognormal kriging for the assessment of reliability in groundwater quality control observation networks

USGS Publications Warehouse

Candela, L.; Olea, R.A.; Custodio, E.

1988-01-01

Groundwater quality observation networks are examples of discontinuous sampling on variables presenting spatial continuity and highly skewed frequency distributions. Anywhere in the aquifer, lognormal kriging provides estimates of the variable being sampled and a standard error of the estimate. The average and the maximum standard error within the network can be used to dynamically improve the network sampling efficiency or find a design able to assure a given reliability level. The approach does not require the formulation of any physical model for the aquifer or any actual sampling of hypothetical configurations. A case study is presented using the network monitoring salty water intrusion into the Llobregat delta confined aquifer, Barcelona, Spain. The variable chloride concentration used to trace the intrusion exhibits sudden changes within short distances which make the standard error fairly invariable to changes in sampling pattern and to substantial fluctuations in the number of wells. ?? 1988.

Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data.

PubMed

Dosso, Stan E; Nielsen, Peter L

2002-01-01

This paper applies the new method of fast Gibbs sampling (FGS) to estimate the uncertainties of seabed geoacoustic parameters in a broadband, shallow-water acoustic survey, with the goal of interpreting the survey results and validating the method for experimental data. FGS applies a Bayesian approach to geoacoustic inversion based on sampling the posterior probability density to estimate marginal probability distributions and parameter covariances. This requires knowledge of the statistical distribution of the data errors, including both measurement and theory errors, which is generally not available. Invoking the simplifying assumption of independent, identically distributed Gaussian errors allows a maximum-likelihood estimate of the data variance and leads to a practical inversion algorithm. However, it is necessary to validate these assumptions, i.e., to verify that the parameter uncertainties obtained represent meaningful estimates. To this end, FGS is applied to a geoacoustic experiment carried out at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. The parameter uncertainties estimated via FGS are validated by comparison with: (i) the variability in the results of inverting multiple independent data sets collected during the experiment; (ii) the results of FGS inversion of synthetic test cases designed to simulate the experiment and data errors; and (iii) the available geophysical ground truth. Comparisons are carried out for a number of different source bandwidths, ranges, and levels of prior information, and indicate that FGS provides reliable and stable uncertainty estimates for the geoacoustic inverse problem.

Methods for accurate estimation of net discharge in a tidal channel

USGS Publications Warehouse

Simpson, M.R.; Bland, R.

2000-01-01

Accurate estimates of net residual discharge in tidally affected rivers and estuaries are possible because of recently developed ultrasonic discharge measurement techniques. Previous discharge estimates using conventional mechanical current meters and methods based on stage/discharge relations or water slope measurements often yielded errors that were as great as or greater than the computed residual discharge. Ultrasonic measurement methods consist of: 1) the use of ultrasonic instruments for the measurement of a representative 'index' velocity used for in situ estimation of mean water velocity and 2) the use of the acoustic Doppler current discharge measurement system to calibrate the index velocity measurement data. Methods used to calibrate (rate) the index velocity to the channel velocity measured using the Acoustic Doppler Current Profiler are the most critical factors affecting the accuracy of net discharge estimation. The index velocity first must be related to mean channel velocity and then used to calculate instantaneous channel discharge. Finally, discharge is low-pass filtered to remove the effects of the tides. An ultrasonic velocity meter discharge-measurement site in a tidally affected region of the Sacramento-San Joaquin Rivers was used to study the accuracy of the index velocity calibration procedure. Calibration data consisting of ultrasonic velocity meter index velocity and concurrent acoustic Doppler discharge measurement data were collected during three time periods. Two sets of data were collected during a spring tide (monthly maximum tidal current) and one of data collected during a neap tide (monthly minimum tidal current). The relative magnitude of instrumental errors, acoustic Doppler discharge measurement errors, and calibration errors were evaluated. Calibration error was found to be the most significant source of error in estimating net discharge. Using a comprehensive calibration method, net discharge estimates developed from the three sets of calibration data differed by less than an average of 4 cubic meters per second, or less than 0.5% of a typical peak tidal discharge rate of 750 cubic meters per second.

Sampling design optimization for spatial functions

USGS Publications Warehouse

Olea, R.A.

1984-01-01

A new procedure is presented for minimizing the sampling requirements necessary to estimate a mappable spatial function at a specified level of accuracy. The technique is based on universal kriging, an estimation method within the theory of regionalized variables. Neither actual implementation of the sampling nor universal kriging estimations are necessary to make an optimal design. The average standard error and maximum standard error of estimation over the sampling domain are used as global indices of sampling efficiency. The procedure optimally selects those parameters controlling the magnitude of the indices, including the density and spatial pattern of the sample elements and the number of nearest sample elements used in the estimation. As an illustration, the network of observation wells used to monitor the water table in the Equus Beds of Kansas is analyzed and an improved sampling pattern suggested. This example demonstrates the practical utility of the procedure, which can be applied equally well to other spatial sampling problems, as the procedure is not limited by the nature of the spatial function. ?? 1984 Plenum Publishing Corporation.

The effect of respiratory induced density variations on non-TOF PET quantitation in the lung.

PubMed

Holman, Beverley F; Cuplov, Vesna; Hutton, Brian F; Groves, Ashley M; Thielemans, Kris

2016-04-21

Accurate PET quantitation requires a matched attenuation map. Obtaining matched CT attenuation maps in the thorax is difficult due to the respiratory cycle which causes both motion and density changes. Unlike with motion, little attention has been given to the effects of density changes in the lung on PET quantitation. This work aims to explore the extent of the errors caused by pulmonary density attenuation map mismatch on dynamic and static parameter estimates. Dynamic XCAT phantoms were utilised using clinically relevant (18)F-FDG and (18)F-FMISO time activity curves for all organs within the thorax to estimate the expected parameter errors. The simulations were then validated with PET data from 5 patients suffering from idiopathic pulmonary fibrosis who underwent PET/Cine-CT. The PET data were reconstructed with three gates obtained from the Cine-CT and the average Cine-CT. The lung TACs clearly displayed differences between true and measured curves with error depending on global activity distribution at the time of measurement. The density errors from using a mismatched attenuation map were found to have a considerable impact on PET quantitative accuracy. Maximum errors due to density mismatch were found to be as high as 25% in the XCAT simulation. Differences in patient derived kinetic parameter estimates and static concentration between the extreme gates were found to be as high as 31% and 14%, respectively. Overall our results show that respiratory associated density errors in the attenuation map affect quantitation throughout the lung, not just regions near boundaries. The extent of this error is dependent on the activity distribution in the thorax and hence on the tracer and time of acquisition. Consequently there may be a significant impact on estimated kinetic parameters throughout the lung.

The effect of respiratory induced density variations on non-TOF PET quantitation in the lung

NASA Astrophysics Data System (ADS)

Holman, Beverley F.; Cuplov, Vesna; Hutton, Brian F.; Groves, Ashley M.; Thielemans, Kris

2016-04-01

Accurate PET quantitation requires a matched attenuation map. Obtaining matched CT attenuation maps in the thorax is difficult due to the respiratory cycle which causes both motion and density changes. Unlike with motion, little attention has been given to the effects of density changes in the lung on PET quantitation. This work aims to explore the extent of the errors caused by pulmonary density attenuation map mismatch on dynamic and static parameter estimates. Dynamic XCAT phantoms were utilised using clinically relevant 18F-FDG and 18F-FMISO time activity curves for all organs within the thorax to estimate the expected parameter errors. The simulations were then validated with PET data from 5 patients suffering from idiopathic pulmonary fibrosis who underwent PET/Cine-CT. The PET data were reconstructed with three gates obtained from the Cine-CT and the average Cine-CT. The lung TACs clearly displayed differences between true and measured curves with error depending on global activity distribution at the time of measurement. The density errors from using a mismatched attenuation map were found to have a considerable impact on PET quantitative accuracy. Maximum errors due to density mismatch were found to be as high as 25% in the XCAT simulation. Differences in patient derived kinetic parameter estimates and static concentration between the extreme gates were found to be as high as 31% and 14%, respectively. Overall our results show that respiratory associated density errors in the attenuation map affect quantitation throughout the lung, not just regions near boundaries. The extent of this error is dependent on the activity distribution in the thorax and hence on the tracer and time of acquisition. Consequently there may be a significant impact on estimated kinetic parameters throughout the lung.

Theoretical investigation on the mass loss impact on asteroseismic grid-based estimates of mass, radius, and age for RGB stars

NASA Astrophysics Data System (ADS)

Valle, G.; Dell'Omodarme, M.; Prada Moroni, P. G.; Degl'Innocenti, S.

2018-01-01

Aims: We aim to perform a theoretical evaluation of the impact of the mass loss indetermination on asteroseismic grid based estimates of masses, radii, and ages of stars in the red giant branch (RGB) phase. Methods: We adopted the SCEPtER pipeline on a grid spanning the mass range [0.8; 1.8] M⊙. As observational constraints, we adopted the star effective temperatures, the metallicity [Fe/H], the average large frequency spacing Δν, and the frequency of maximum oscillation power νmax. The mass loss was modelled following a Reimers parametrization with the two different efficiencies η = 0.4 and η = 0.8. Results: In the RGB phase, the average random relative error (owing only to observational uncertainty) on mass and age estimates is about 8% and 30% respectively. The bias in mass and age estimates caused by the adoption of a wrong mass loss parameter in the recovery is minor for the vast majority of the RGB evolution. The biases get larger only after the RGB bump. In the last 2.5% of the RGB lifetime the error on the mass determination reaches 6.5% becoming larger than the random error component in this evolutionary phase. The error on the age estimate amounts to 9%, that is, equal to the random error uncertainty. These results are independent of the stellar metallicity [Fe/H] in the explored range. Conclusions: Asteroseismic-based estimates of stellar mass, radius, and age in the RGB phase can be considered mass loss independent within the range (η ∈ [0.0,0.8]) as long as the target is in an evolutionary phase preceding the RGB bump.

Evaluation of the fishery status for King Soldier Bream Argyrops spinifer in Pakistan using the software CEDA and ASPIC

NASA Astrophysics Data System (ADS)

Memon, Aamir Mahmood; Liu, Qun; Memon, Khadim Hussain; Baloch, Wazir Ali; Memon, Asfandyar; Baset, Abdul

2015-07-01

Catch and effort data were analyzed to estimate the maximum sustainable yield (MSY) of King Soldier Bream, Argyrops spinifer (Forsskål, 1775, Family: Sparidae), and to evaluate the present status of the fish stocks exploited in Pakistani waters. The catch and effort data for the 25-years period 1985-2009 were analyzed using two computer software packages, CEDA (catch and effort data analysis) and ASPIC (a surplus production model incorporating covariates). The maximum catch of 3 458 t was observed in 1988 and the minimum catch of 1 324 t in 2005, while the average annual catch of A. spinifer over the 25 years was 2 500 t. The surplus production models of Fox, Schaefer, and Pella Tomlinson under three error assumptions of normal, log-normal and gamma are in the CEDA package and the two surplus models of Fox and logistic are in the ASPIC package. In CEDA, the MSY was estimated by applying the initial proportion (IP) of 0.8, because the starting catch was approximately 80% of the maximum catch. Except for gamma, because gamma showed maximization failures, the estimated results of MSY using CEDA with the Fox surplus production model and two error assumptions, were 1 692.08 t ( R 2=0.572) and 1 694.09 t ( R 2=0.606), respectively, and from the Schaefer and the Pella Tomlinson models with two error assumptions were 2 390.95 t ( R 2=0.563), and 2 380.06 t ( R 2=0.605), respectively. The MSY estimated by the Fox model was conservatively compared to the Schaefer and Pella Tomlinson models. The MSY values from Schaefer and Pella Tomlinson models were the same. The computed values of MSY using the ASPIC computer software program with the two surplus production models of Fox and logistic were 1 498 t ( R 2=0.917), and 2 488 t ( R 2=0.897) respectively. The estimated values of MSY using CEDA were about 1 700-2 400 t and the values from ASPIC were 1 500-2 500 t. The estimates output by the CEDA and the ASPIC packages indicate that the stock is overfished, and needs some effective management to reduce the fishing effort of the species in Pakistani waters.

Efficient estimation of Pareto model: Some modified percentile estimators.

PubMed

Bhatti, Sajjad Haider; Hussain, Shahzad; Ahmad, Tanvir; Aslam, Muhammad; Aftab, Muhammad; Raza, Muhammad Ali

2018-01-01

The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant

NASA Astrophysics Data System (ADS)

Winiarek, Victor; Bocquet, Marc; Saunier, Olivier; Mathieu, Anne

2012-03-01

A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativity of the measurements, those that are instrumental, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. We propose to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We apply the method to the estimation of the Fukushima Daiichi source term using activity concentrations in the air. The results are compared to an L-curve estimation technique and to Desroziers's scheme. The total reconstructed activities significantly depend on the chosen method. Because of the poor observability of the Fukushima Daiichi emissions, these methods provide lower bounds for cesium-137 and iodine-131 reconstructed activities. These lower bound estimates, 1.2 × 1016 Bq for cesium-137, with an estimated standard deviation range of 15%-20%, and 1.9 - 3.8 × 1017 Bq for iodine-131, with an estimated standard deviation range of 5%-10%, are of the same order of magnitude as those provided by the Japanese Nuclear and Industrial Safety Agency and about 5 to 10 times less than the Chernobyl atmospheric releases.

The maximum entropy method of moments and Bayesian probability theory

NASA Astrophysics Data System (ADS)

Bretthorst, G. Larry

2013-08-01

The problem of density estimation occurs in many disciplines. For example, in MRI it is often necessary to classify the types of tissues in an image. To perform this classification one must first identify the characteristics of the tissues to be classified. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) may be generated. In a given tissue type there is no single intensity that characterizes the tissue, rather there is a distribution of intensities. Often this distributions can be characterized by a Gaussian, but just as often it is much more complicated. Either way, estimating the distribution of intensities is an inference problem. In the case of a Gaussian distribution, one must estimate the mean and standard deviation. However, in the Non-Gaussian case the shape of the density function itself must be inferred. Three common techniques for estimating density functions are binned histograms [1, 2], kernel density estimation [3, 4], and the maximum entropy method of moments [5, 6]. In the introduction, the maximum entropy method of moments will be reviewed. Some of its problems and conditions under which it fails will be discussed. Then in later sections, the functional form of the maximum entropy method of moments probability distribution will be incorporated into Bayesian probability theory. It will be shown that Bayesian probability theory solves all of the problems with the maximum entropy method of moments. One gets posterior probabilities for the Lagrange multipliers, and, finally, one can put error bars on the resulting estimated density function.

Reverse Transcription Errors and RNA-DNA Differences at Short Tandem Repeats.

PubMed

Fungtammasan, Arkarachai; Tomaszkiewicz, Marta; Campos-Sánchez, Rebeca; Eckert, Kristin A; DeGiorgio, Michael; Makova, Kateryna D

2016-10-01

Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA-DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a Caenorhabditis elegans data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization.

PubMed

Du, Xinxin; Tan, Kok Kiong

2016-05-01

Vehicle lane-level localization is a fundamental technology in autonomous driving. To achieve accurate and consistent performance, a common approach is to use the LIDAR technology. However, it is expensive and computational demanding, and thus not a practical solution in many situations. This paper proposes a stereovision system, which is of low cost, yet also able to achieve high accuracy and consistency. It integrates a new lane line detection algorithm with other lane marking detectors to effectively identify the correct lane line markings. It also fits multiple road models to improve accuracy. An effective stereo 3D reconstruction method is proposed to estimate vehicle localization. The estimation consistency is further guaranteed by a new particle filter framework, which takes vehicle dynamics into account. Experiment results based on image sequences taken under different visual conditions showed that the proposed system can identify the lane line markings with 98.6% accuracy. The maximum estimation error of the vehicle distance to lane lines is 16 cm in daytime and 26 cm at night, and the maximum estimation error of its moving direction with respect to the road tangent is 0.06 rad in daytime and 0.12 rad at night. Due to its high accuracy and consistency, the proposed system can be implemented in autonomous driving vehicles as a practical solution to vehicle lane-level localization.

Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: statistical and systematic error budgets for future experiments

DOE PAGES

Raghunathan, Srinivasan; Patil, Sanjaykumar; Baxter, Eric J.; ...

2017-08-25

We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at low noise levels in temperature, this optimal estimator outperforms the standard quadratic estimator by a factor of two. For polarization, we show that the Stokes Q/U maps can be used instead of the traditional E- and B-mode maps without losing information. We test and quantify the bias in the recovered lensing mass for a comprehensive list of potential systematic errors. Using realistic simulations, wemore » examine the cluster mass uncertainties from CMB-cluster lensing as a function of an experiment’s beam size and noise level. We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT, and Simons Array experiments with 10,000 clusters and less than 1% for the CMB-S4 experiment with a sample containing 100,000 clusters. The mass constraints from CMB polarization are very sensitive to the experimental beam size and map noise level: for a factor of three reduction in either the beam size or noise level, the lensing signal-to-noise improves by roughly a factor of two.« less

Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: statistical and systematic error budgets for future experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raghunathan, Srinivasan; Patil, Sanjaykumar; Baxter, Eric J.

We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at low noise levels in temperature, this optimal estimator outperforms the standard quadratic estimator by a factor of two. For polarization, we show that the Stokes Q/U maps can be used instead of the traditional E- and B-mode maps without losing information. We test and quantify the bias in the recovered lensing mass for a comprehensive list of potential systematic errors. Using realistic simulations, wemore » examine the cluster mass uncertainties from CMB-cluster lensing as a function of an experiment’s beam size and noise level. We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT, and Simons Array experiments with 10,000 clusters and less than 1% for the CMB-S4 experiment with a sample containing 100,000 clusters. The mass constraints from CMB polarization are very sensitive to the experimental beam size and map noise level: for a factor of three reduction in either the beam size or noise level, the lensing signal-to-noise improves by roughly a factor of two.« less

Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: statistical and systematic error budgets for future experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raghunathan, Srinivasan; Patil, Sanjaykumar; Bianchini, Federico

We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at low noise levels in temperature, this optimal estimator outperforms the standard quadratic estimator by a factor of two. For polarization, we show that the Stokes Q/U maps can be used instead of the traditional E- and B-mode maps without losing information. We test and quantify the bias in the recovered lensing mass for a comprehensive list of potential systematic errors. Using realistic simulations, wemore » examine the cluster mass uncertainties from CMB-cluster lensing as a function of an experiment's beam size and noise level. We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT, and Simons Array experiments with 10,000 clusters and less than 1% for the CMB-S4 experiment with a sample containing 100,000 clusters. The mass constraints from CMB polarization are very sensitive to the experimental beam size and map noise level: for a factor of three reduction in either the beam size or noise level, the lensing signal-to-noise improves by roughly a factor of two.« less

Maximum type I error rate inflation from sample size reassessment when investigators are blind to treatment labels.

PubMed

Żebrowska, Magdalena; Posch, Martin; Magirr, Dominic

2016-05-30

Consider a parallel group trial for the comparison of an experimental treatment to a control, where the second-stage sample size may depend on the blinded primary endpoint data as well as on additional blinded data from a secondary endpoint. For the setting of normally distributed endpoints, we demonstrate that this may lead to an inflation of the type I error rate if the null hypothesis holds for the primary but not the secondary endpoint. We derive upper bounds for the inflation of the type I error rate, both for trials that employ random allocation and for those that use block randomization. We illustrate the worst-case sample size reassessment rule in a case study. For both randomization strategies, the maximum type I error rate increases with the effect size in the secondary endpoint and the correlation between endpoints. The maximum inflation increases with smaller block sizes if information on the block size is used in the reassessment rule. Based on our findings, we do not question the well-established use of blinded sample size reassessment methods with nuisance parameter estimates computed from the blinded interim data of the primary endpoint. However, we demonstrate that the type I error rate control of these methods relies on the application of specific, binding, pre-planned and fully algorithmic sample size reassessment rules and does not extend to general or unplanned sample size adjustments based on blinded data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Estimation of sampling error uncertainties in observed surface air temperature change in China

NASA Astrophysics Data System (ADS)

Hua, Wei; Shen, Samuel S. P.; Weithmann, Alexander; Wang, Huijun

2017-08-01

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K2 while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K2. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)-1 occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)-1 in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.

Dynamic Method for Identifying Collected Sample Mass

NASA Technical Reports Server (NTRS)

Carson, John

2008-01-01

G-Sample is designed for sample collection missions to identify the presence and quantity of sample material gathered by spacecraft equipped with end effectors. The software method uses a maximum-likelihood estimator to identify the collected sample's mass based on onboard force-sensor measurements, thruster firings, and a dynamics model of the spacecraft. This makes sample mass identification a computation rather than a process requiring additional hardware. Simulation examples of G-Sample are provided for spacecraft model configurations with a sample collection device mounted on the end of an extended boom. In the absence of thrust knowledge errors, the results indicate that G-Sample can identify the amount of collected sample mass to within 10 grams (with 95-percent confidence) by using a force sensor with a noise and quantization floor of 50 micrometers. These results hold even in the presence of realistic parametric uncertainty in actual spacecraft inertia, center-of-mass offset, and first flexibility modes. Thrust profile knowledge is shown to be a dominant sensitivity for G-Sample, entering in a nearly one-to-one relationship with the final mass estimation error. This means thrust profiles should be well characterized with onboard accelerometers prior to sample collection. An overall sample-mass estimation error budget has been developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Forensic Entomology: Evaluating Uncertainty Associated With Postmortem Interval (PMI) Estimates With Ecological Models.

PubMed

Faris, A M; Wang, H-H; Tarone, A M; Grant, W E

2016-05-31

Estimates of insect age can be informative in death investigations and, when certain assumptions are met, can be useful for estimating the postmortem interval (PMI). Currently, the accuracy and precision of PMI estimates is unknown, as error can arise from sources of variation such as measurement error, environmental variation, or genetic variation. Ecological models are an abstract, mathematical representation of an ecological system that can make predictions about the dynamics of the real system. To quantify the variation associated with the pre-appearance interval (PAI), we developed an ecological model that simulates the colonization of vertebrate remains by Cochliomyia macellaria (Fabricius) (Diptera: Calliphoridae), a primary colonizer in the southern United States. The model is based on a development data set derived from a local population and represents the uncertainty in local temperature variability to address PMI estimates at local sites. After a PMI estimate is calculated for each individual, the model calculates the maximum, minimum, and mean PMI, as well as the range and standard deviation for stadia collected. The model framework presented here is one manner by which errors in PMI estimates can be addressed in court when no empirical data are available for the parameter of interest. We show that PAI is a potential important source of error and that an ecological model is one way to evaluate its impact. Such models can be re-parameterized with any development data set, PAI function, temperature regime, assumption of interest, etc., to estimate PMI and quantify uncertainty that arises from specific prediction systems. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Corrected score estimation in the proportional hazards model with misclassified discrete covariates

PubMed Central

Zucker, David M.; Spiegelman, Donna

2013-01-01

SUMMARY We consider Cox proportional hazards regression when the covariate vector includes error-prone discrete covariates along with error-free covariates, which may be discrete or continuous. The misclassification in the discrete error-prone covariates is allowed to be of any specified form. Building on the work of Nakamura and his colleagues, we present a corrected score method for this setting. The method can handle all three major study designs (internal validation design, external validation design, and replicate measures design), both functional and structural error models, and time-dependent covariates satisfying a certain ‘localized error’ condition. We derive the asymptotic properties of the method and indicate how to adjust the covariance matrix of the regression coefficient estimates to account for estimation of the misclassification matrix. We present the results of a finite-sample simulation study under Weibull survival with a single binary covariate having known misclassification rates. The performance of the method described here was similar to that of related methods we have examined in previous works. Specifically, our new estimator performed as well as or, in a few cases, better than the full Weibull maximum likelihood estimator. We also present simulation results for our method for the case where the misclassification probabilities are estimated from an external replicate measures study. Our method generally performed well in these simulations. The new estimator has a broader range of applicability than many other estimators proposed in the literature, including those described in our own earlier work, in that it can handle time-dependent covariates with an arbitrary misclassification structure. We illustrate the method on data from a study of the relationship between dietary calcium intake and distal colon cancer. PMID:18219700

Re-assessing accumulated oxygen deficit in middle-distance runners.

PubMed

Bickham, D; Le Rossignol, P; Gibbons, C; Russell, A P

2002-12-01

The purpose of this study was to re-assess the accumulated oxygen deficit (AOD), incorporating recent methodological improvements i.e., 4 min submaximal tests spread above and below the lactate threshold (LT). We Investigated the Influence of the VO2 -speed regression, on the precision of the estimated total energy demand and AOD. utilising different numbers of regression points and including measurement errors. Seven trained middle-distance runners (mean +/- SD age: 25.3 +/- 5.4y, mass: 73.7 +/- 4.3kg. VO2max 64.4 +/- 6.1 mL x kg(-1) x min(-1)) completed a VO2max, LT, 10 x 4 min exercise tests (above and below LT) and high-intensity exhaustive tests. The VO2 -speed regression was developed using 10 submaximal points and a forced y-intercept value. The average precision (measured as the width of 95% confidence Interval) for the estimated total energy demand using this regression was 7.8mL O2 Eq x kg(-1) x min(-1). There was a two-fold decrease in precision of estimated total energy demand with the Inclusion of measurement errors from the metabolic system. The mean AOD value was 43.3 mL O2 Eq x kg(-1) (upper and lower 95% CI 32.1 and 54.5mL o2 Eq x kg(-1) respectively). Converting the 95% CI for estimated total energy demand to AOD or including maximum possible measurement errors amplified the error associated with the estimated total energy demand. No significant difference in AOD variables were found, using 10,4 or 2 regression points with a forced y-intercept. For practical purposes we recommend the use of 4 submaximal values with a y-intercept. Using 95% CIs and calculating error highlighted possible error in estimating AOD. Without accurate data collection, increased variability could decrease the accuracy of the AOD as shown by a 95% CI of the AOD.

Wind friction parametrisation used in emission models for wastewater treatment plants: A critical review.

PubMed

Prata, Ademir A; Santos, Jane M; Timchenko, Victoria; Reis, Neyval C; Stuetz, Richard M

2017-11-01

Emission models are widely applied tools for estimating atmospheric emissions from wastewater treatment plants (WWTPs). The friction velocity u ∗ is a key variable for the modelling of emissions from passive liquid surfaces in WWTPs. This work evaluated different parametrisations of u ∗ for passive liquid surfaces at the scale of WWTP units, which present relatively small fetches, based on available wind friction and wave data measured at wind-wave tanks (fetches spanning from approximately 3 to 100 m, and wind speeds from 2 to 17 m s -1 ). The empirical correlation by Smith (1980; J. Phys. Oceanogr. 10, 709-726), which has been frequently adopted in air emission models (despite the fact that it was originally derived for the ocean) presented a general tendency to overestimate u ∗ , with significant (although not extreme) relative errors (mean and maximum errors of 13.5% and 36.6%, respectively); the use of Charnock's relation, with Charnock constant 0.010, performed in a very similar manner (mean and maximum errors of 13.3% and 37.8%, respectively). Better estimates of u ∗ were achieved by parametrisations based on the significant wave steepness. Simplified correlations between the wind drag and the non-dimensional fetch were obtained. An approach was devised, comprising the use of Charnock's relation (with Charnock constant 0.010) and of these simplified correlations, depending on the ranges of frequency of the peak waves, fetch and wind speed. The proposed approach predicted u ∗ with improved accuracy (mean, maximum and 95%-percentile relative errors of 6.6%, 16.7% and 13.9%, respectively), besides being able to incorporate the influence of the fetch in the wind drag, thus taking into account the size of the tanks in the WWTPs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Maximum-likelihood methods in wavefront sensing: stochastic models and likelihood functions

PubMed Central

Barrett, Harrison H.; Dainty, Christopher; Lara, David

2008-01-01

Maximum-likelihood (ML) estimation in wavefront sensing requires careful attention to all noise sources and all factors that influence the sensor data. We present detailed probability density functions for the output of the image detector in a wavefront sensor, conditional not only on wavefront parameters but also on various nuisance parameters. Practical ways of dealing with nuisance parameters are described, and final expressions for likelihoods and Fisher information matrices are derived. The theory is illustrated by discussing Shack–Hartmann sensors, and computational requirements are discussed. Simulation results show that ML estimation can significantly increase the dynamic range of a Shack–Hartmann sensor with four detectors and that it can reduce the residual wavefront error when compared with traditional methods. PMID:17206255

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter.

PubMed

Choi, Jihoon; Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-09-13

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter

PubMed Central

Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-01-01

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected. PMID:28902154

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.

2011-01-01

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

NASA Technical Reports Server (NTRS)

Davis, John H.

1993-01-01

Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

Accurate acceleration of kinetic Monte Carlo simulations through the modification of rate constants.

PubMed

Chatterjee, Abhijit; Voter, Arthur F

2010-05-21

We present a novel computational algorithm called the accelerated superbasin kinetic Monte Carlo (AS-KMC) method that enables a more efficient study of rare-event dynamics than the standard KMC method while maintaining control over the error. In AS-KMC, the rate constants for processes that are observed many times are lowered during the course of a simulation. As a result, rare processes are observed more frequently than in KMC and the time progresses faster. We first derive error estimates for AS-KMC when the rate constants are modified. These error estimates are next employed to develop a procedure for lowering process rates with control over the maximum error. Finally, numerical calculations are performed to demonstrate that the AS-KMC method captures the correct dynamics, while providing significant CPU savings over KMC in most cases. We show that the AS-KMC method can be employed with any KMC model, even when no time scale separation is present (although in such cases no computational speed-up is observed), without requiring the knowledge of various time scales present in the system.

Systematic errors in temperature estimates from MODIS data covering the western Palearctic and their impact on a parasite development model.

PubMed

Alonso-Carné, Jorge; García-Martín, Alberto; Estrada-Peña, Agustin

2013-11-01

The modelling of habitat suitability for parasites is a growing area of research due to its association with climate change and ensuing shifts in the distribution of infectious diseases. Such models depend on remote sensing data and require accurate, high-resolution temperature measurements. The temperature is critical for accurate estimation of development rates and potential habitat ranges for a given parasite. The MODIS sensors aboard the Aqua and Terra satellites provide high-resolution temperature data for remote sensing applications. This paper describes comparative analysis of MODIS-derived temperatures relative to ground records of surface temperature in the western Palaearctic. The results show that MODIS overestimated maximum temperature values and underestimated minimum temperatures by up to 5-6 °C. The combined use of both Aqua and Terra datasets provided the most accurate temperature estimates around latitude 35-44° N, with an overestimation during spring-summer months and an underestimation in autumn-winter. Errors in temperature estimation were associated with specific ecological regions within the target area as well as technical limitations in the temporal and orbital coverage of the satellites (e.g. sensor limitations and satellite transit times). We estimated error propagation of temperature uncertainties in parasite habitat suitability models by comparing outcomes of published models. Error estimates reached 36% of annual respective measurements depending on the model used. Our analysis demonstrates the importance of adequate image processing and points out the limitations of MODIS temperature data as inputs into predictive models concerning parasite lifecycles.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2016-08-01

In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992-2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850 MJ m-2 day-1, 1.184 MJ m-2 day-1, 9.58% and 0.935, respectively.

Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study.

PubMed

Chou, C P; Bentler, P M; Satorra, A

1991-11-01

Research studying robustness of maximum likelihood (ML) statistics in covariance structure analysis has concluded that test statistics and standard errors are biased under severe non-normality. An estimation procedure known as asymptotic distribution free (ADF), making no distributional assumption, has been suggested to avoid these biases. Corrections to the normal theory statistics to yield more adequate performance have also been proposed. This study compares the performance of a scaled test statistic and robust standard errors for two models under several non-normal conditions and also compares these with the results from ML and ADF methods. Both ML and ADF test statistics performed rather well in one model and considerably worse in the other. In general, the scaled test statistic seemed to behave better than the ML test statistic and the ADF statistic performed the worst. The robust and ADF standard errors yielded more appropriate estimates of sampling variability than the ML standard errors, which were usually downward biased, in both models under most of the non-normal conditions. ML test statistics and standard errors were found to be quite robust to the violation of the normality assumption when data had either symmetric and platykurtic distributions, or non-symmetric and zero kurtotic distributions.

Composite Linear Models | Division of Cancer Prevention

Cancer.gov

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA

ERIC Educational Resources Information Center

Brosseau-Liard, Patricia E.; Savalei, Victoria; Li, Libo

2012-01-01

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have…

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

NASA Astrophysics Data System (ADS)

Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

2018-02-01

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points.

A Gompertzian model with random effects to cervical cancer growth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mazlan, Mazma Syahidatul Ayuni; Rosli, Norhayati

2015-05-15

In this paper, a Gompertzian model with random effects is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via maximum likehood estimation. We apply 4-stage Runge-Kutta (SRK4) for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of the cervical cancer growth. Low values of root mean-square error (RMSE) of Gompertzian model with random effect indicate good fits.

Maximum-Likelihood Estimation for Frequency-Modulated Continuous-Wave Laser Ranging using Photon-Counting Detectors

DTIC Science & Technology

2013-03-21

instruments where frequency estimates are calcu- lated from coherently detected fields, e.g., coherent Doppler LIDAR . Our CRB results reveal that the best...wave coherent lidar using an optical field correlation detection method,” Opt. Rev. 5, 310–314 (1998). 8. H. P. Yuen and V. W. S. Chan, “Noise in...2170–2180 (2007). 13. T. J. Karr, “Atmospheric phase error in coherent laser radar,” IEEE Trans. Antennas Propag. 55, 1122–1133 (2007). 14. Throughout

A Method for Estimating Missing Hourly Temperatures Using Daily Maximum and Minimum Temperatures

DTIC Science & Technology

1991-08-01

work documented by USAFETAC/PR-90/006, S/urt-Termn Hourl ’y Iernpcrature Interlyolaion, by Mal Wvalter F . Miller, December 1990. In his study, Miller...temperatures for the missing hours and concluded that the best model %as one developed by Hoogenboom and [luck (1986). The Hoogcnboom/Huck model uses a...mean of the error estimate, was determined from the following equation: - 7)) BIAS = 1 N", f (14) where the difference between the observed hourly

A Bayesian model for estimating multi-state disease progression.

PubMed

Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

2017-02-01

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis.

PubMed

Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C

2014-12-01

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Enhancing interferometer phase estimation, sensing sensitivity, and resolution using robust entangled states

NASA Astrophysics Data System (ADS)

Smith, James F.

2017-11-01

With the goal of designing interferometers and interferometer sensors, e.g., LADARs with enhanced sensitivity, resolution, and phase estimation, states using quantum entanglement are discussed. These states include N00N states, plain M and M states (PMMSs), and linear combinations of M and M states (LCMMS). Closed form expressions for the optimal detection operators; visibility, a measure of the state's robustness to loss and noise; a resolution measure; and phase estimate error, are provided in closed form. The optimal resolution for the maximum visibility and minimum phase error are found. For the visibility, comparisons between PMMSs, LCMMS, and N00N states are provided. For the minimum phase error, comparisons between LCMMS, PMMSs, N00N states, separate photon states (SPSs), the shot noise limit (SNL), and the Heisenberg limit (HL) are provided. A representative collection of computational results illustrating the superiority of LCMMS when compared to PMMSs and N00N states is given. It is found that for a resolution 12 times the classical result LCMMS has visibility 11 times that of N00N states and 4 times that of PMMSs. For the same case, the minimum phase error for LCMMS is 10.7 times smaller than that of PMMS and 29.7 times smaller than that of N00N states.

Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

PubMed

Sztepanacz, Jacqueline L; Blows, Mark W

2017-07-01

The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix. Copyright © 2017 by the Genetics Society of America.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3; The Map and Related Decoding Algirithms

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc

1998-01-01

In a coded communication system with equiprobable signaling, MLD minimizes the word error probability and delivers the most likely codeword associated with the corresponding received sequence. This decoding has two drawbacks. First, minimization of the word error probability is not equivalent to minimization of the bit error probability. Therefore, MLD becomes suboptimum with respect to the bit error probability. Second, MLD delivers a hard-decision estimate of the received sequence, so that information is lost between the input and output of the ML decoder. This information is important in coded schemes where the decoded sequence is further processed, such as concatenated coding schemes, multi-stage and iterative decoding schemes. In this chapter, we first present a decoding algorithm which both minimizes bit error probability, and provides the corresponding soft information at the output of the decoder. This algorithm is referred to as the MAP (maximum aposteriori probability) decoding algorithm.

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

NASA Astrophysics Data System (ADS)

Khaleghi, Mohammad Reza; Varvani, Javad

2018-02-01

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

An intersecting chord method for minimum circumscribed sphere and maximum inscribed sphere evaluations of sphericity error

NASA Astrophysics Data System (ADS)

Liu, Fei; Xu, Guanghua; Zhang, Qing; Liang, Lin; Liu, Dan

2015-11-01

As one of the Geometrical Product Specifications that are widely applied in industrial manufacturing and measurement, sphericity error can synthetically scale a 3D structure and reflects the machining quality of a spherical workpiece. Following increasing demands in the high motion performance of spherical parts, sphericity error is becoming an indispensable component in the evaluation of form error. However, the evaluation of sphericity error is still considered to be a complex mathematical issue, and the related research studies on the development of available models are lacking. In this paper, an intersecting chord method is first proposed to solve the minimum circumscribed sphere and maximum inscribed sphere evaluations of sphericity error. This new modelling method leverages chord relationships to replace the characteristic points, thereby significantly reducing the computational complexity and improving the computational efficiency. Using the intersecting chords to generate a virtual centre, the reference sphere in two concentric spheres is simplified as a space intersecting structure. The position of the virtual centre on the space intersecting structure is determined by characteristic chords, which may reduce the deviation between the virtual centre and the centre of the reference sphere. In addition,two experiments are used to verify the effectiveness of the proposed method with real datasets from the Cartesian coordinates. The results indicate that the estimated errors are in perfect agreement with those of the published methods. Meanwhile, the computational efficiency is improved. For the evaluation of the sphericity error, the use of high performance computing is a remarkable change.

Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples.

PubMed

Henshall, John M; Dierens, Leanne; Sellars, Melony J

2014-09-02

While much attention has focused on the development of high-density single nucleotide polymorphism (SNP) assays, the costs of developing and running low-density assays have fallen dramatically. This makes it feasible to develop and apply SNP assays for agricultural species beyond the major livestock species. Although low-cost low-density assays may not have the accuracy of the high-density assays widely used in human and livestock species, we show that when combined with statistical analysis approaches that use quantitative instead of discrete genotypes, their utility may be improved. The data used in this study are from a 63-SNP marker Sequenom® iPLEX Platinum panel for the Black Tiger shrimp, for which high-density SNP assays are not currently available. For quantitative genotypes that could be estimated, in 5% of cases the most likely genotype for an individual at a SNP had a probability of less than 0.99. Matrix formulations of maximum likelihood equations for parentage assignment were developed for the quantitative genotypes and also for discrete genotypes perturbed by an assumed error term. Assignment rates that were based on maximum likelihood with quantitative genotypes were similar to those based on maximum likelihood with perturbed genotypes but, for more than 50% of cases, the two methods resulted in individuals being assigned to different families. Treating genotypes as quantitative values allows the same analysis framework to be used for pooled samples of DNA from multiple individuals. Resulting correlations between allele frequency estimates from pooled DNA and individual samples were consistently greater than 0.90, and as high as 0.97 for some pools. Estimates of family contributions to the pools based on quantitative genotypes in pooled DNA had a correlation of 0.85 with estimates of contributions from DNA-derived pedigree. Even with low numbers of SNPs of variable quality, parentage testing and family assignment from pooled samples are sufficiently accurate to provide useful information for a breeding program. Treating genotypes as quantitative values is an alternative to perturbing genotypes using an assumed error distribution, but can produce very different results. An understanding of the distribution of the error is required for SNP genotyping platforms.

Estimation of Uncertainties in Stage-Discharge Curve for an Experimental Himalayan Watershed

NASA Astrophysics Data System (ADS)

Kumar, V.; Sen, S.

2016-12-01

Various water resource projects developed on rivers originating from the Himalayan region, the "Water Tower of Asia", plays an important role on downstream development. Flow measurements at the desired river site are very critical for river engineers and hydrologists for water resources planning and management, flood forecasting, reservoir operation and flood inundation studies. However, an accurate discharge assessment of these mountainous rivers is costly, tedious and frequently dangerous to operators during flood events. Currently, in India, discharge estimation is linked to stage-discharge relationship known as rating curve. This relationship would be affected by a high degree of uncertainty. Estimating the uncertainty of rating curve remains a relevant challenge because it is not easy to parameterize. Main source of rating curve uncertainty are errors because of incorrect discharge measurement, variation in hydraulic conditions and depth measurement. In this study our objective is to obtain best parameters of rating curve that fit the limited record of observations and to estimate uncertainties at different depth obtained from rating curve. The rating curve parameters of standard power law are estimated for three different streams of Aglar watershed located in lesser Himalayas by maximum-likelihood estimator. Quantification of uncertainties in the developed rating curves is obtained from the estimate of variances and covariances of the rating curve parameters. Results showed that the uncertainties varied with catchment behavior with error varies between 0.006-1.831 m3/s. Discharge uncertainty in the Aglar watershed streams significantly depend on the extent of extrapolation outside the range of observed water levels. Extrapolation analysis confirmed that more than 15% for maximum discharges and 5% for minimum discharges are not strongly recommended for these mountainous gauging sites.

Estimates of the absolute error and a scheme for an approximate solution to scheduling problems

NASA Astrophysics Data System (ADS)

Lazarev, A. A.

2009-02-01

An approach is proposed for estimating absolute errors and finding approximate solutions to classical NP-hard scheduling problems of minimizing the maximum lateness for one or many machines and makespan is minimized. The concept of a metric (distance) between instances of the problem is introduced. The idea behind the approach is, given the problem instance, to construct another instance for which an optimal or approximate solution can be found at the minimum distance from the initial instance in the metric introduced. Instead of solving the original problem (instance), a set of approximating polynomially/pseudopolynomially solvable problems (instances) are considered, an instance at the minimum distance from the given one is chosen, and the resulting schedule is then applied to the original instance.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

NASA Technical Reports Server (NTRS)

da Silva, Arlindo; Redder, Christopher

2010-01-01

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

Using MERRA Gridded Innovation for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

NASA Astrophysics Data System (ADS)

da Silva, A.; Redder, C. R.

2010-12-01

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The Project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

Constrained Maximum Likelihood Estimation of Relative Abundances of Protein Conformation in a Heterogeneous Mixture from Small Angle X-Ray Scattering Intensity Measurements

PubMed Central

Onuk, A. Emre; Akcakaya, Murat; Bardhan, Jaydeep P.; Erdogmus, Deniz; Brooks, Dana H.; Makowski, Lee

2015-01-01

In this paper, we describe a model for maximum likelihood estimation (MLE) of the relative abundances of different conformations of a protein in a heterogeneous mixture from small angle X-ray scattering (SAXS) intensities. To consider cases where the solution includes intermediate or unknown conformations, we develop a subset selection method based on k-means clustering and the Cramér-Rao bound on the mixture coefficient estimation error to find a sparse basis set that represents the space spanned by the measured SAXS intensities of the known conformations of a protein. Then, using the selected basis set and the assumptions on the model for the intensity measurements, we show that the MLE model can be expressed as a constrained convex optimization problem. Employing the adenylate kinase (ADK) protein and its known conformations as an example, and using Monte Carlo simulations, we demonstrate the performance of the proposed estimation scheme. Here, although we use 45 crystallographically determined experimental structures and we could generate many more using, for instance, molecular dynamics calculations, the clustering technique indicates that the data cannot support the determination of relative abundances for more than 5 conformations. The estimation of this maximum number of conformations is intrinsic to the methodology we have used here. PMID:26924916

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging.

PubMed

Zhang, Shuanghui; Liu, Yongxiang; Li, Xiang; Bi, Guoan

2016-04-28

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors

PubMed Central

Liu, Shibin

2018-01-01

Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors. PMID:29789448

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV-infected infants and toddlers.

PubMed

Zhao, Wei; Cella, Massimo; Della Pasqua, Oscar; Burger, David; Jacqz-Aigrain, Evelyne

2012-04-01

Abacavir is used to treat HIV infection in both adults and children. The recommended paediatric dose is 8 mg kg(-1) twice daily up to a maximum of 300 mg twice daily. Weight was identified as the central covariate influencing pharmacokinetics of abacavir in children. A population pharmacokinetic model was developed to describe both once and twice daily pharmacokinetic profiles of abacavir in infants and toddlers. Standard dosage regimen is associated with large interindividual variability in abacavir concentrations. A maximum a posteriori probability Bayesian estimator of AUC(0-) (t) based on three time points (0, 1 or 2, and 3 h) is proposed to support area under the concentration-time curve (AUC) targeted individualized therapy in infants and toddlers. To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration-time curve (AUC) targeted dosage and individualize therapy. The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation-estimation method. The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 () h−1 (RSE 6.3%), apparent central volume of distribution 4.94 () (RSE 28.7%), apparent peripheral volume of distribution 8.12 () (RSE14.2%), apparent intercompartment clearance 1.25 () h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t. The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC(0-) (t) was developed from the final model and can be used routinely to optimize individual dosing. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Stochastic control system parameter identifiability

NASA Technical Reports Server (NTRS)

Lee, C. H.; Herget, C. J.

1975-01-01

The parameter identification problem of general discrete time, nonlinear, multiple input/multiple output dynamic systems with Gaussian white distributed measurement errors is considered. The knowledge of the system parameterization was assumed to be known. Concepts of local parameter identifiability and local constrained maximum likelihood parameter identifiability were established. A set of sufficient conditions for the existence of a region of parameter identifiability was derived. A computation procedure employing interval arithmetic was provided for finding the regions of parameter identifiability. If the vector of the true parameters is locally constrained maximum likelihood (CML) identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the constrained maximum likelihood estimation sequence will converge to the vector of true parameters.

Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting

PubMed Central

Kamneva, Olga K; Rosenberg, Noah A

2017-01-01

Hybridization events generate reticulate species relationships, giving rise to species networks rather than species trees. We report a comparative study of consensus, maximum parsimony, and maximum likelihood methods of species network reconstruction using gene trees simulated assuming a known species history. We evaluate the role of the divergence time between species involved in a hybridization event, the relative contributions of the hybridizing species, and the error in gene tree estimation. When gene tree discordance is mostly due to hybridization and not due to incomplete lineage sorting (ILS), most of the methods can detect even highly skewed hybridization events between highly divergent species. For recent divergences between hybridizing species, when the influence of ILS is sufficiently high, likelihood methods outperform parsimony and consensus methods, which erroneously identify extra hybridizations. The more sophisticated likelihood methods, however, are affected by gene tree errors to a greater extent than are consensus and parsimony. PMID:28469378

Development and application of the maximum entropy method and other spectral estimation techniques

NASA Astrophysics Data System (ADS)

King, W. R.

1980-09-01

This summary report is a collection of four separate progress reports prepared under three contracts, which are all sponsored by the Office of Naval Research in Arlington, Virginia. This report contains the results of investigations into the application of the maximum entropy method (MEM), a high resolution, frequency and wavenumber estimation technique. The report also contains a description of two, new, stable, high resolution spectral estimation techniques that is provided in the final report section. Many examples of wavenumber spectral patterns for all investigated techniques are included throughout the report. The maximum entropy method is also known as the maximum entropy spectral analysis (MESA) technique, and both names are used in the report. Many MEM wavenumber spectral patterns are demonstrated using both simulated and measured radar signal and noise data. Methods for obtaining stable MEM wavenumber spectra are discussed, broadband signal detection using the MEM prediction error transform (PET) is discussed, and Doppler radar narrowband signal detection is demonstrated using the MEM technique. It is also shown that MEM cannot be applied to randomly sampled data. The two new, stable, high resolution, spectral estimation techniques discussed in the final report section, are named the Wiener-King and the Fourier spectral estimation techniques. The two new techniques have a similar derivation based upon the Wiener prediction filter, but the two techniques are otherwise quite different. Further development of the techniques and measurement of the technique spectral characteristics is recommended for subsequent investigation.

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.

PubMed

Butland, Barbara K; Armstrong, Ben; Atkinson, Richard W; Wilkinson, Paul; Heal, Mathew R; Doherty, Ruth M; Vieno, Massimo

2013-11-13

Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003-2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

Maximum likelihood estimation in calibrating a stereo camera setup.

PubMed

Muijtjens, A M; Roos, J M; Arts, T; Hasman, A

1999-02-01

Motion and deformation of the cardiac wall may be measured by following the positions of implanted radiopaque markers in three dimensions, using two x-ray cameras simultaneously. Regularly, calibration of the position measurement system is obtained by registration of the images of a calibration object, containing 10-20 radiopaque markers at known positions. Unfortunately, an accidental change of the position of a camera after calibration requires complete recalibration. Alternatively, redundant information in the measured image positions of stereo pairs can be used for calibration. Thus, a separate calibration procedure can be avoided. In the current study a model is developed that describes the geometry of the camera setup by five dimensionless parameters. Maximum Likelihood (ML) estimates of these parameters were obtained in an error analysis. It is shown that the ML estimates can be found by application of a nonlinear least squares procedure. Compared to the standard unweighted least squares procedure, the ML method resulted in more accurate estimates without noticeable bias. The accuracy of the ML method was investigated in relation to the object aperture. The reconstruction problem appeared well conditioned as long as the object aperture is larger than 0.1 rad. The angle between the two viewing directions appeared to be the parameter that was most likely to cause major inaccuracies in the reconstruction of the 3-D positions of the markers. Hence, attempts to improve the robustness of the method should primarily focus on reduction of the error in this parameter.

Tests for detecting overdispersion in models with measurement error in covariates.

PubMed

Yang, Yingsi; Wong, Man Yu

2015-11-30

Measurement error in covariates can affect the accuracy in count data modeling and analysis. In overdispersion identification, the true mean-variance relationship can be obscured under the influence of measurement error in covariates. In this paper, we propose three tests for detecting overdispersion when covariates are measured with error: a modified score test and two score tests based on the proposed approximate likelihood and quasi-likelihood, respectively. The proposed approximate likelihood is derived under the classical measurement error model, and the resulting approximate maximum likelihood estimator is shown to have superior efficiency. Simulation results also show that the score test based on approximate likelihood outperforms the test based on quasi-likelihood and other alternatives in terms of empirical power. By analyzing a real dataset containing the health-related quality-of-life measurements of a particular group of patients, we demonstrate the importance of the proposed methods by showing that the analyses with and without measurement error correction yield significantly different results. Copyright © 2015 John Wiley & Sons, Ltd.

A two-dimensional, finite-difference model of the high plains aquifer in southern South Dakota

USGS Publications Warehouse

Kolm, K.E.; Case, H. L.

1983-01-01

The High Plains aquifer is the principal source of water for irrigation, industry, municipalities, and domestic use in south-central South Dakota. The aquifer, composed of upper sandstone units of the Arikaree Formation, and the overlying Ogallala and Sand Hills Formations, was simulated using a two-dimensional, finite-difference computer model. The maximum difference between simulated and measured potentiometric heads was less than 60 feet (1- to 4-percent error). Two-thirds of the simulated potentiometric heads were within 26 feet of the measured values (3-percent error). The estimated saturated thickness, computed from simulated potentiometric heads, was within 25-percent error of the known saturated thickness for 95 percent of the study area. (USGS)

A numerical identifiability test for state-space models--application to optimal experimental design.

PubMed

Hidalgo, M E; Ayesa, E

2001-01-01

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No. 1 calibration, in order to estimate the maximum specific growth rate microH and the concentration of heterotrophic biomass XBH.

Characterizing Protease Specificity: How Many Substrates Do We Need?

PubMed Central

Schauperl, Michael; Fuchs, Julian E.; Waldner, Birgit J.; Huber, Roland G.; Kramer, Christian; Liedl, Klaus R.

2015-01-01

Calculation of cleavage entropies allows to quantify, map and compare protease substrate specificity by an information entropy based approach. The metric intrinsically depends on the number of experimentally determined substrates (data points). Thus a statistical analysis of its numerical stability is crucial to estimate the systematic error made by estimating specificity based on a limited number of substrates. In this contribution, we show the mathematical basis for estimating the uncertainty in cleavage entropies. Sets of cleavage entropies are calculated using experimental cleavage data and modeled extreme cases. By analyzing the underlying mathematics and applying statistical tools, a linear dependence of the metric in respect to 1/n was found. This allows us to extrapolate the values to an infinite number of samples and to estimate the errors. Analyzing the errors, a minimum number of 30 substrates was found to be necessary to characterize substrate specificity, in terms of amino acid variability, for a protease (S4-S4’) with an uncertainty of 5 percent. Therefore, we encourage experimental researchers in the protease field to record specificity profiles of novel proteases aiming to identify at least 30 peptide substrates of maximum sequence diversity. We expect a full characterization of protease specificity helpful to rationalize biological functions of proteases and to assist rational drug design. PMID:26559682

Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

NASA Astrophysics Data System (ADS)

Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

2017-06-01

Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

A feasibility study on estimation of tissue mixture contributions in 3D arterial spin labeling sequence

NASA Astrophysics Data System (ADS)

Liu, Yang; Pu, Huangsheng; Zhang, Xi; Li, Baojuan; Liang, Zhengrong; Lu, Hongbing

2017-03-01

Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. To obtain accurate CBF estimation, the contribution of each tissue type in the mixture is desirable. In general, this can be obtained according to the registration of ASL and structural image in current ASL studies. This approach can obtain probability of each tissue type inside each voxel, but it also introduces error, which include error of registration algorithm and imaging itself error in scanning of ASL and structural image. Therefore, estimation of mixture percentage directly from ASL data is greatly needed. Under the assumption that ASL signal followed the Gaussian distribution and each tissue type is independent, a maximum a posteriori expectation-maximization (MAP-EM) approach was formulated to estimate the contribution of each tissue type to the observed perfusion signal at each voxel. Considering the sensitivity of MAP-EM to the initialization, an approximately accurate initialization was obtain using 3D Fuzzy c-means method. Our preliminary results demonstrated that the GM and WM pattern across the perfusion image can be sufficiently visualized by the voxel-wise tissue mixtures, which may be promising for the diagnosis of various brain diseases.

Viterbi equalization for long-distance, high-speed underwater laser communication

NASA Astrophysics Data System (ADS)

Hu, Siqi; Mi, Le; Zhou, Tianhua; Chen, Weibiao

2017-07-01

In long-distance, high-speed underwater laser communication, because of the strong absorption and scattering processes, the laser pulse is stretched with the increase in communication distance and the decrease in water clarity. The maximum communication bandwidth is limited by laser-pulse stretching. Improving the communication rate increases the intersymbol interference (ISI). To reduce the effect of ISI, the Viterbi equalization (VE) algorithm is used to estimate the maximum-likelihood receiving sequence. The Monte Carlo method is used to simulate the stretching of the received laser pulse and the maximum communication rate at a wavelength of 532 nm in Jerlov IB and Jerlov II water channels with communication distances of 80, 100, and 130 m, respectively. The high-data rate communication performance for the VE and hard-decision algorithms is compared. The simulation results show that the VE algorithm can be used to reduce the ISI by selecting the minimum error path. The trade-off between the high-data rate communication performance and minor bit-error rate performance loss makes VE a promising option for applications in long-distance, high-speed underwater laser communication systems.

Quantum State Tomography via Linear Regression Estimation

PubMed Central

Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

2013-01-01

A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China.

PubMed

Chen, Li; Gao, Shuang; Zhang, Hui; Sun, Yanling; Ma, Zhenxing; Vedal, Sverre; Mao, Jian; Bai, Zhipeng

2018-05-03

Concentrations of particulate matter with aerodynamic diameter <2.5 μm (PM 2.5 ) are relatively high in China. Estimation of PM 2.5 exposure is complex because PM 2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM 2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R 2  = 0.82 and root mean square error (RMSE) of 4.6 μg/m 3 . Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R 2 increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM 2.5 model developed to date for China. Copyright © 2018. Published by Elsevier Ltd.

Maximum Likelihood Estimation of Spectra Information from Multiple Independent Astrophysics Data Sets

NASA Technical Reports Server (NTRS)

Howell, Leonard W., Jr.; Six, N. Frank (Technical Monitor)

2002-01-01

The Maximum Likelihood (ML) statistical theory required to estimate spectra information from an arbitrary number of astrophysics data sets produced by vastly different science instruments is developed in this paper. This theory and its successful implementation will facilitate the interpretation of spectral information from multiple astrophysics missions and thereby permit the derivation of superior spectral information based on the combination of data sets. The procedure is of significant value to both existing data sets and those to be produced by future astrophysics missions consisting of two or more detectors by allowing instrument developers to optimize each detector's design parameters through simulation studies in order to design and build complementary detectors that will maximize the precision with which the science objectives may be obtained. The benefits of this ML theory and its application is measured in terms of the reduction of the statistical errors (standard deviations) of the spectra information using the multiple data sets in concert as compared to the statistical errors of the spectra information when the data sets are considered separately, as well as any biases resulting from poor statistics in one or more of the individual data sets that might be reduced when the data sets are combined.

Modeling and Validation of the Three Dimensional Deflection of an MRI-Compatible Magnetically-Actuated Steerable Catheter

PubMed Central

Liu, Taoming; Poirot, Nate Lombard; Franson, Dominique; Seiberlich, Nicole; Griswold, Mark A.; Çavuşoğlu, M. Cenk

2016-01-01

Objective This paper presents the three dimensional kinematic modeling of a novel steerable robotic ablation catheter system. The catheter, embedded with a set of current-carrying micro-coils, is actuated by the magnetic forces generated by the magnetic field of the magnetic resonance imaging (MRI) scanner. Methods This paper develops a 3D model of the MRI actuated steerable catheter system by using finite differences approach. For each finite segment, a quasi-static torque-deflection equilibrium equation is calculated using beam theory. By using the deflection displacements and torsion angles, the kinematic model of the catheter system is derived. Results The proposed models are validated by comparing the simulation results of the proposed model with the experimental results of a hardware prototype of the catheter design. The maximum tip deflection error is 4.70 mm and the maximum root-mean-square (RMS) error of the shape estimation is 3.48 mm. Conclusion The results demonstrate that the proposed model can successfully estimate the deflection motion of the catheter. Significance The presented three dimensional deflection model of the magnetically controlled catheter design paves the way to efficient control of the robotic catheter for treatment of atrial fibrillation. PMID:26731519

Estimation After a Group Sequential Trial.

PubMed

Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel; Kenward, Michael G; Tsiatis, Anastasios A; Davidian, Marie; Verbeke, Geert

2015-10-01

Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al (2012) and Milanzi et al (2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, N = n or N = 2 n . In this paper, we consider the more practically useful setting of sample sizes in a the finite set { n 1 , n 2 , …, n L }. It is shown that the sample average is then a justifiable estimator , in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why simulations can give the false impression of bias in the sample average when considered conditional upon the sample size. The consequence is that no corrections need to be made to estimators following sequential trials. When small-sample bias is of concern, the conditional likelihood estimator provides a relatively straightforward modification to the sample average. Finally, it is shown that classical likelihood-based standard errors and confidence intervals can be applied, obviating the need for technical corrections.

Cost effectiveness of the stream-gaging program in South Carolina

USGS Publications Warehouse

Barker, A.C.; Wright, B.C.; Bennett, C.S.

1985-01-01

The cost effectiveness of the stream-gaging program in South Carolina was documented for the 1983 water yr. Data uses and funding sources were identified for the 76 continuous stream gages currently being operated in South Carolina. The budget of $422,200 for collecting and analyzing streamflow data also includes the cost of operating stage-only and crest-stage stations. The streamflow records for one stream gage can be determined by alternate, less costly methods, and should be discontinued. The remaining 75 stations should be maintained in the program for the foreseeable future. The current policy for the operation of the 75 stations including the crest-stage and stage-only stations would require a budget of $417,200/yr. The average standard error of estimation of streamflow records is 16.9% for the present budget with missing record included. However, the standard error of estimation would decrease to 8.5% if complete streamflow records could be obtained. It was shown that the average standard error of estimation of 16.9% could be obtained at the 75 sites with a budget of approximately $395,000 if the gaging resources were redistributed among the gages. A minimum budget of $383,500 is required to operate the program; a budget less than this does not permit proper service and maintenance of the gages and recorders. At the minimum budget, the average standard error is 18.6%. The maximum budget analyzed was $850,000, which resulted in an average standard error of 7.6 %. (Author 's abstract)

A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada.

PubMed

Ménard, Richard; Deshaies-Jacques, Martin; Gasset, Nicolas

2016-09-01

An objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth-Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and the [Formula: see text] diagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, or [Formula: see text] should be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values. This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme.

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-07-01

Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

Pencil beam proton radiography using a multilayer ionization chamber

NASA Astrophysics Data System (ADS)

Farace, Paolo; Righetto, Roberto; Meijers, Arturs

2016-06-01

A pencil beam proton radiography (PR) method, using a commercial multilayer ionization chamber (MLIC) integrated with a treatment planning system (TPS) was developed. A Giraffe (IBA Dosimetry) MLIC (±0.5 mm accuracy) was used to obtain pencil beam PR by delivering spots uniformly positioned at a 5.0 mm distance in a 9  ×  9 square of spots. PRs of an electron-density (with tissue-equivalent inserts) phantom and a head phantom were acquired. The integral depth dose (IDD) curves of the delivered spots were computed by the TPS in a volume of water simulating the MLIC, and virtually added to the CT at the exit side of the phantoms. For each spot, measured and calculated IDD were overlapped in order to compute a map of range errors. On the head-phantom, the maximum dose from PR acquisition was estimated. Additionally, on the head phantom the impact on the range errors map was estimated in case of a 1 mm position misalignment. In the electron-density phantom, range errors were within 1 mm in the soft-tissue rods, but greater in the dense-rod. In the head-phantom the range errors were  -0.9  ±  2.7 mm on the whole map and within 1 mm in the brain area. On both phantoms greater errors were observed at inhomogeneity interfaces, due to sensitivity to small misalignment, and inaccurate TPS dose computation. The effect of the 1 mm misalignment was clearly visible on the range error map and produced an increased spread of range errors (-1.0  ±  3.8 mm on the whole map). The dose to the patient for such PR acquisitions would be acceptable as the maximum dose to the head phantom was  <2cGyE. By the described 2D method, allowing to discriminate misalignments, range verification can be performed in selected areas to implement an in vivo quality assurance program.

Pencil beam proton radiography using a multilayer ionization chamber.

PubMed

Farace, Paolo; Righetto, Roberto; Meijers, Arturs

2016-06-07

A pencil beam proton radiography (PR) method, using a commercial multilayer ionization chamber (MLIC) integrated with a treatment planning system (TPS) was developed. A Giraffe (IBA Dosimetry) MLIC (±0.5 mm accuracy) was used to obtain pencil beam PR by delivering spots uniformly positioned at a 5.0 mm distance in a 9  ×  9 square of spots. PRs of an electron-density (with tissue-equivalent inserts) phantom and a head phantom were acquired. The integral depth dose (IDD) curves of the delivered spots were computed by the TPS in a volume of water simulating the MLIC, and virtually added to the CT at the exit side of the phantoms. For each spot, measured and calculated IDD were overlapped in order to compute a map of range errors. On the head-phantom, the maximum dose from PR acquisition was estimated. Additionally, on the head phantom the impact on the range errors map was estimated in case of a 1 mm position misalignment. In the electron-density phantom, range errors were within 1 mm in the soft-tissue rods, but greater in the dense-rod. In the head-phantom the range errors were  -0.9  ±  2.7 mm on the whole map and within 1 mm in the brain area. On both phantoms greater errors were observed at inhomogeneity interfaces, due to sensitivity to small misalignment, and inaccurate TPS dose computation. The effect of the 1 mm misalignment was clearly visible on the range error map and produced an increased spread of range errors (-1.0  ±  3.8 mm on the whole map). The dose to the patient for such PR acquisitions would be acceptable as the maximum dose to the head phantom was  <2cGyE. By the described 2D method, allowing to discriminate misalignments, range verification can be performed in selected areas to implement an in vivo quality assurance program.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust.

PubMed

Sanatkar, M R; Scoglio, C; Natarajan, B; Isard, S A; Garrett, K A

2015-07-01

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

Effects of model error on control of large flexible space antenna with comparisons of decoupled and linear quadratic regulator control procedures

NASA Technical Reports Server (NTRS)

Hamer, H. A.; Johnson, K. G.

1986-01-01

An analysis was performed to determine the effects of model error on the control of a large flexible space antenna. Control was achieved by employing two three-axis control-moment gyros (CMG's) located on the antenna column. State variables were estimated by including an observer in the control loop that used attitude and attitude-rate sensors on the column. Errors were assumed to exist in the individual model parameters: modal frequency, modal damping, mode slope (control-influence coefficients), and moment of inertia. Their effects on control-system performance were analyzed either for (1) nulling initial disturbances in the rigid-body modes, or (2) nulling initial disturbances in the first three flexible modes. The study includes the effects on stability, time to null, and control requirements (defined as maximum torque and total momentum), as well as on the accuracy of obtaining initial estimates of the disturbances. The effects on the transients of the undisturbed modes are also included. The results, which are compared for decoupled and linear quadratic regulator (LQR) control procedures, are shown in tabular form, parametric plots, and as sample time histories of modal-amplitude and control responses. Results of the analysis showed that the effects of model errors on the control-system performance were generally comparable for both control procedures. The effect of mode-slope error was the most serious of all model errors.

Inventory and mapping of flood inundation using interactive digital image analysis techniques

USGS Publications Warehouse

Rohde, Wayne G.; Nelson, Charles A.; Taranik, J.V.

1979-01-01

LANDSAT digital data and color infra-red photographs were used in a multiphase sampling scheme to estimate the area of agricultural land affected by a flood. The LANDSAT data were classified with a maximum likelihood algorithm. Stratification of the LANDSAT data, prior to classification, greatly reduced misclassification errors. The classification results were used to prepare a map overlay showing the areal extent of flooding. These data also provided statistics required to estimate sample size in a two phase sampling scheme, and provided quick, accurate estimates of areas flooded for the first phase. The measurements made in the second phase, based on ground data and photo-interpretation, were used with two phase sampling statistics to estimate the area of agricultural land affected by flooding These results show that LANDSAT digital data can be used to prepare map overlays showing the extent of flooding on agricultural land and, with two phase sampling procedures, can provide acreage estimates with sampling errors of about 5 percent. This procedure provides a technique for rapidly assessing the areal extent of flood conditions on agricultural land and would provide a basis for designing a sampling framework to estimate the impact of flooding on crop production.

A digital clock recovery algorithm based on chromatic dispersion and polarization mode dispersion feedback dual phase detection for coherent optical transmission systems

NASA Astrophysics Data System (ADS)

Liu, Bo; Xin, Xiangjun; Zhang, Lijia; Wang, Fu; Zhang, Qi

2018-02-01

A new feedback symbol timing recovery technique using timing estimation joint equalization is proposed for digital receivers with two samples/symbol or higher sampling rate. Different from traditional methods, the clock recovery algorithm in this paper adopts another algorithm distinguishing the phases of adjacent symbols, so as to accurately estimate the timing offset based on the adjacent signals with the same phase. The addition of the module for eliminating phase modulation interference before timing estimation further reduce the variance, thus resulting in a smoothed timing estimate. The Mean Square Error (MSE) and Bit Error Rate (BER) of the resulting timing estimate are simulated to allow a satisfactory estimation performance. The obtained clock tone performance is satisfactory for MQAM modulation formats and the Roll-off Factor (ROF) close to 0. In the back-to-back system, when ROF= 0, the maximum of MSE obtained with the proposed approach reaches 0 . 0125. After 100-km fiber transmission, BER decreases to 10-3 with ROF= 0 and OSNR = 11 dB. With the increase in ROF, the performances of MSE and BER become better.

Modeling Nonlinear Errors in Surface Electromyography Due To Baseline Noise: A New Methodology

PubMed Central

Law, Laura Frey; Krishnan, Chandramouli; Avin, Keith

2010-01-01

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2 – 40 % maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low. PMID:20869716

Mining pharmacovigilance data using Bayesian logistic regression with James-Stein type shrinkage estimation.

PubMed

An, Lihua; Fung, Karen Y; Krewski, Daniel

2010-09-01

Spontaneous adverse event reporting systems are widely used to identify adverse reactions to drugs following their introduction into the marketplace. In this article, a James-Stein type shrinkage estimation strategy was developed in a Bayesian logistic regression model to analyze pharmacovigilance data. This method is effective in detecting signals as it combines information and borrows strength across medically related adverse events. Computer simulation demonstrated that the shrinkage estimator is uniformly better than the maximum likelihood estimator in terms of mean squared error. This method was used to investigate the possible association of a series of diabetic drugs and the risk of cardiovascular events using data from the Canada Vigilance Online Database.

Degradation data analysis based on a generalized Wiener process subject to measurement error

NASA Astrophysics Data System (ADS)

Li, Junxing; Wang, Zhihua; Zhang, Yongbo; Fu, Huimin; Liu, Chengrui; Krishnaswamy, Sridhar

2017-09-01

Wiener processes have received considerable attention in degradation modeling over the last two decades. In this paper, we propose a generalized Wiener process degradation model that takes unit-to-unit variation, time-correlated structure and measurement error into considerations simultaneously. The constructed methodology subsumes a series of models studied in the literature as limiting cases. A simple method is given to determine the transformed time scale forms of the Wiener process degradation model. Then model parameters can be estimated based on a maximum likelihood estimation (MLE) method. The cumulative distribution function (CDF) and the probability distribution function (PDF) of the Wiener process with measurement errors are given based on the concept of the first hitting time (FHT). The percentiles of performance degradation (PD) and failure time distribution (FTD) are also obtained. Finally, a comprehensive simulation study is accomplished to demonstrate the necessity of incorporating measurement errors in the degradation model and the efficiency of the proposed model. Two illustrative real applications involving the degradation of carbon-film resistors and the wear of sliding metal are given. The comparative results show that the constructed approach can derive a reasonable result and an enhanced inference precision.

Quantifying precision of in situ length and weight measurements of fish

USGS Publications Warehouse

Gutreuter, S.; Krzoska, D.J.

1994-01-01

We estimated and compared errors in field-made (in situ) measurements of lengths and weights of fish. We made three measurements of length and weight on each of 33 common carp Cyprinus carpio, and on each of a total of 34 bluegills Lepomis macrochirus and black crappies Pomoxis nigromaculatus. Maximum total lengths of all fish were measured to the nearest 1 mm on a conventional measuring board. The bluegills and black crappies (85–282 mm maximum total length) were weighed to the nearest 1 g on a 1,000-g spring-loaded scale. The common carp (415–600 mm maximum total length) were weighed to the nearest 0.05 kg on a 20-kg spring-loaded scale. We present a statistical model for comparison of coefficients of variation of length (Cl ) and weight (Cw ). Expected Cl was near zero and constant across mean length, indicating that length can be measured with good precision in the field. Expected Cw decreased with increasing mean length, and was larger than expected Cl by 5.8 to over 100 times for the bluegills and black crappies, and by 3 to over 20 times for the common carp. Unrecognized in situ weighing errors bias the apparent content of unique information in weight, which is the information not explained by either length or measurement error. We recommend procedures to circumvent effects of weighing errors, including elimination of unnecessary weighing from routine monitoring programs. In situ weighing must be conducted with greater care than is common if the content of unique and nontrivial information in weight is to be correctly identified.

The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions

NASA Astrophysics Data System (ADS)

Loaiciga, Hugo A.; MariñO, Miguel A.

1987-01-01

The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.

Scanning linear estimation: improvements over region of interest (ROI) methods

NASA Astrophysics Data System (ADS)

Kupinski, Meredith K.; Clarkson, Eric W.; Barrett, Harrison H.

2013-03-01

In tomographic medical imaging, a signal activity is typically estimated by summing voxels from a reconstructed image. We introduce an alternative estimation scheme that operates on the raw projection data and offers a substantial improvement, as measured by the ensemble mean-square error (EMSE), when compared to using voxel values from a maximum-likelihood expectation-maximization (MLEM) reconstruction. The scanning-linear (SL) estimator operates on the raw projection data and is derived as a special case of maximum-likelihood estimation with a series of approximations to make the calculation tractable. The approximated likelihood accounts for background randomness, measurement noise and variability in the parameters to be estimated. When signal size and location are known, the SL estimate of signal activity is unbiased, i.e. the average estimate equals the true value. By contrast, unpredictable bias arising from the null functions of the imaging system affect standard algorithms that operate on reconstructed data. The SL method is demonstrated for two different tasks: (1) simultaneously estimating a signal’s size, location and activity; (2) for a fixed signal size and location, estimating activity. Noisy projection data are realistically simulated using measured calibration data from the multi-module multi-resolution small-animal SPECT imaging system. For both tasks, the same set of images is reconstructed using the MLEM algorithm (80 iterations), and the average and maximum values within the region of interest (ROI) are calculated for comparison. This comparison shows dramatic improvements in EMSE for the SL estimates. To show that the bias in ROI estimates affects not only absolute values but also relative differences, such as those used to monitor the response to therapy, the activity estimation task is repeated for three different signal sizes.

Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species

USGS Publications Warehouse

Then, Amy Y.; Hoenig, John M; Hall, Norman G.; Hewitt, David A.

2015-01-01

Many methods have been developed in the last 70 years to predict the natural mortality rate, M, of a stock based on empirical evidence from comparative life history studies. These indirect or empirical methods are used in most stock assessments to (i) obtain estimates of M in the absence of direct information, (ii) check on the reasonableness of a direct estimate of M, (iii) examine the range of plausible M estimates for the stock under consideration, and (iv) define prior distributions for Bayesian analyses. The two most cited empirical methods have appeared in the literature over 2500 times to date. Despite the importance of these methods, there is no consensus in the literature on how well these methods work in terms of prediction error or how their performance may be ranked. We evaluate estimators based on various combinations of maximum age (tmax), growth parameters, and water temperature by seeing how well they reproduce >200 independent, direct estimates of M. We use tenfold cross-validation to estimate the prediction error of the estimators and to rank their performance. With updated and carefully reviewed data, we conclude that a tmax-based estimator performs the best among all estimators evaluated. The tmax-based estimators in turn perform better than the Alverson–Carney method based on tmax and the von Bertalanffy K coefficient, Pauly’s method based on growth parameters and water temperature and methods based just on K. It is possible to combine two independent methods by computing a weighted mean but the improvement over the tmax-based methods is slight. Based on cross-validation prediction error, model residual patterns, model parsimony, and biological considerations, we recommend the use of a tmax-based estimator (M=4.899tmax−0.916">M=4.899t−0.916maxM=4.899tmax−0.916, prediction error = 0.32) when possible and a growth-based method (M=4.118K0.73L∞−0.33">M=4.118K0.73L−0.33∞M=4.118K0.73L∞−0.33 , prediction error = 0.6, length in cm) otherwise.

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

NASA Astrophysics Data System (ADS)

Li, Xinya; Deng, Zhiqun Daniel; Rauchenstein, Lynn T.; Carlson, Thomas J.

2016-04-01

Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications and technologies such as radar, sonar, the Global Positioning System, wireless sensor networks, underwater animal tracking, mobile communications, and multimedia.

DtaRefinery: a software tool for elimination of systematic errors from parent ion mass measurements in tandem mass spectra datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petyuk, Vladislav A.; Mayampurath, Anoop M.; Monroe, Matthew E.

2009-12-16

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and MS/MS fragmentation have become widely available in recent years and have allowed for sig-nificantly better discrimination between true and false MS/MS pep-tide identifications by applying relatively narrow windows for maxi-mum allowable deviations for parent ion mass measurements. To fully gain the advantage of highly accurate parent ion mass meas-urements, it is important to limit systematic mass measurement errors. The DtaRefinery software tool can correct systematic errors in parent ion masses by reading a set of fragmentation spectra, searching for MS/MS peptide identifications, then fitting a model that canmore » estimate systematic errors, and removing them. This results in a new fragmentation spectrum file with updated parent ion masses.« less

A preliminary estimate of changing calcrete carbon storage on land since the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Adams, J. M.; Post, W. M.

1999-05-01

The glacial-to-interglacial shift in land carbon storage is important in understanding the global carbon cycle and history of the climate system. While organic carbon storage on land appears to have been much less than present during the cold, dry glacial maximum, calcrete (soil carbonate) carbon storage would have been greater. Here we attempt a global estimation of this change; we use published figures for present soil carbonate by biome to estimate changing global soil carbonate storage, on the basis of reconstruction of vegetation areas for four timeslices since the Last Glacial Maximum. It appears that there would most likely have been around a 30-45% decrease in calcrete carbon on land accompanying the transition between glacial and interglacial conditions. This represents a change of about 500-400 GtC (outer error limits are estimated at 750-200 GtC) . In order to be weathered into dissolved bicarbonate, this would take up an additional 500-400 GtC (750-200 GtC) in CO 2 from ocean/atmosphere sources. An equivalent amount to the carbonate leaving the caliche reservoir on land may have accumulated in coral reefs and other calcareous marine sediments during the Holocene, liberating an equimolar quantity of CO 2 back into the ocean-atmosphere system as the bicarbonate ion breaks up.

A Comparison of Item Selection Procedures Using Different Ability Estimation Methods in Computerized Adaptive Testing Based on the Generalized Partial Credit Model

ERIC Educational Resources Information Center

Ho, Tsung-Han

2010-01-01

Computerized adaptive testing (CAT) provides a highly efficient alternative to the paper-and-pencil test. By selecting items that match examinees' ability levels, CAT not only can shorten test length and administration time but it can also increase measurement precision and reduce measurement error. In CAT, maximum information (MI) is the most…

TU-AB-202-03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, H; Chen, J; Phillips, J

2016-06-15

Purpose: Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel-specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron-emission tomography (PET) with DIR. Methods: Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET-CT image set. A deformation was digitally applied to the diagnostic CT to create the planningmore » CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT. Results: The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient-based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT-predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch. Conclusion: The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.« less

Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; Huang, Jiaqi; Wei, Daozhi

2016-11-01

This paper investigates the design of a novel estimation-free prescribed performance non-affine control strategy for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) via back-stepping. The proposed control scheme is capable of guaranteeing tracking errors of velocity, altitude, flight-path angle, pitch angle and pitch rate with prescribed performance. By prescribed performance, we mean that the tracking error is limited to a predefined arbitrarily small residual set, with convergence rate no less than a certain constant, exhibiting maximum overshoot less than a given value. Unlike traditional back-stepping designs, there is no need of an affine model in this paper. Moreover, both the tedious analytic and numerical computations of time derivatives of virtual control laws are completely avoided. In contrast to estimation-based strategies, the presented estimation-free controller possesses much lower computational costs, while successfully eliminating the potential problem of parameter drifting. Owing to its independence on an accurate AHV model, the studied methodology exhibits excellent robustness against system uncertainties. Finally, simulation results from a fully nonlinear model clarify and verify the design.

Model uncertainty of various settlement estimation methods in shallow tunnels excavation; case study: Qom subway tunnel

NASA Astrophysics Data System (ADS)

Khademian, Amir; Abdollahipour, Hamed; Bagherpour, Raheb; Faramarzi, Lohrasb

2017-10-01

In addition to the numerous planning and executive challenges, underground excavation in urban areas is always followed by certain destructive effects especially on the ground surface; ground settlement is the most important of these effects for which estimation there exist different empirical, analytical and numerical methods. Since geotechnical models are associated with considerable model uncertainty, this study characterized the model uncertainty of settlement estimation models through a systematic comparison between model predictions and past performance data derived from instrumentation. To do so, the amount of surface settlement induced by excavation of the Qom subway tunnel was estimated via empirical (Peck), analytical (Loganathan and Poulos) and numerical (FDM) methods; the resulting maximum settlement value of each model were 1.86, 2.02 and 1.52 cm, respectively. The comparison of these predicted amounts with the actual data from instrumentation was employed to specify the uncertainty of each model. The numerical model outcomes, with a relative error of 3.8%, best matched the reality and the analytical method, with a relative error of 27.8%, yielded the highest level of model uncertainty.

Evaluation and interpretation of Thematic Mapper ratios in equations for estimating corn growth parameters

NASA Technical Reports Server (NTRS)

Dardner, B. R.; Blad, B. L.; Thompson, D. R.; Henderson, K. E.

1985-01-01

Reflectance and agronomic Thematic Mapper (TM) data were analyzed to determine possible data transformations for evaluating several plant parameters of corn. Three transformation forms were used: the ratio of two TM bands, logarithms of two-band ratios, and normalized differences of two bands. Normalized differences and logarithms of two-band ratios responsed similarly in the equations for estimating the plant growth parameters evaluated in this study. Two-term equations were required to obtain the maximum predictability of percent ground cover, canopy moisture content, and total wet phytomass. Standard error of estimate values were 15-26 percent lower for two-term estimates of these parameters than for one-term estimates. The terms log(TM4/TM2) and (TM4/TM5) produced the maximum predictability for leaf area and dry green leaf weight, respectively. The middle infrared bands TM5 and TM7 are essential for maximizing predictability for all measured plant parameters except leaf area index. The estimating models were evaluated over bare soil to discriminate between equations which are statistically similar. Qualitative interpretations of the resulting prediction equations are consistent with general agronomic and remote sensing theory.

On Inertial Body Tracking in the Presence of Model Calibration Errors

PubMed Central

Miezal, Markus; Taetz, Bertram; Bleser, Gabriele

2016-01-01

In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached inertial measurement units (IMUs). This requires the relative poses of the IMUs w.r.t. the segments—the IMU-to-segment calibrations, subsequently called I2S calibrations—to be known. Since calibration methods based on static poses, movements and manual measurements are still the most widely used, potentially large human-induced calibration errors have to be expected. This work compares three newly developed/adapted extended Kalman filter (EKF) and optimization-based sensor fusion methods with an existing EKF-based method w.r.t. their segment orientation estimation accuracy in the presence of model calibration errors with and without using magnetometer information. While the existing EKF-based method uses a segment-centered kinematic chain biomechanical model and a constant angular acceleration motion model, the newly developed/adapted methods are all based on a free segments model, where each segment is represented with six degrees of freedom in the global frame. Moreover, these methods differ in the assumed motion model (constant angular acceleration, constant angular velocity, inertial data as control input), the state representation (segment-centered, IMU-centered) and the estimation method (EKF, sliding window optimization). In addition to the free segments representation, the optimization-based method also represents each IMU with six degrees of freedom in the global frame. In the evaluation on simulated and real data from a three segment model (an arm), the optimization-based method showed the smallest mean errors, standard deviations and maximum errors throughout all tests. It also showed the lowest dependency on magnetometer information and motion agility. Moreover, it was insensitive w.r.t. I2S position and segment length errors in the tested ranges. Errors in the I2S orientations were, however, linearly propagated into the estimated segment orientations. In the absence of magnetic disturbances, severe model calibration errors and fast motion changes, the newly developed IMU centered EKF-based method yielded comparable results with lower computational complexity. PMID:27455266

Estimating distribution parameters of annual maximum streamflows in Johor, Malaysia using TL-moments approach

NASA Astrophysics Data System (ADS)

Mat Jan, Nur Amalina; Shabri, Ani

2017-01-01

TL-moments approach has been used in an analysis to identify the best-fitting distributions to represent the annual series of maximum streamflow data over seven stations in Johor, Malaysia. The TL-moments with different trimming values are used to estimate the parameter of the selected distributions namely: Three-parameter lognormal (LN3) and Pearson Type III (P3) distribution. The main objective of this study is to derive the TL-moments ( t 1,0), t 1 = 1,2,3,4 methods for LN3 and P3 distributions. The performance of TL-moments ( t 1,0), t 1 = 1,2,3,4 was compared with L-moments through Monte Carlo simulation and streamflow data over a station in Johor, Malaysia. The absolute error is used to test the influence of TL-moments methods on estimated probability distribution functions. From the cases in this study, the results show that TL-moments with four trimmed smallest values from the conceptual sample (TL-moments [4, 0]) of LN3 distribution was the most appropriate in most of the stations of the annual maximum streamflow series in Johor, Malaysia.

A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

PubMed Central

Wang, Chaolong; Schroeder, Kari B.; Rosenberg, Noah A.

2012-01-01

Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets imputed under our model can be investigated in additional subsequent analyses, our method will be useful for preparing data for applications in diverse contexts in population genetics and molecular ecology. PMID:22851645

Portable bioimpedance monitor evaluation for continuous impedance measurements. Towards wearable plethysmography applications.

PubMed

Ferreira, J; Seoane, F; Lindecrantz, K

2013-01-01

Personalised Health Systems (PHS) that could benefit the life quality of the patients as well as decreasing the health care costs for society among other factors are arisen. The purpose of this paper is to study the capabilities of the System-on-Chip Impedance Network Analyser AD5933 performing high speed single frequency continuous bioimpedance measurements. From a theoretical analysis, the minimum continuous impedance estimation time was determined, and the AD5933 with a custom 4-Electrode Analog Front-End (AFE) was used to experimentally determine the maximum continuous impedance estimation frequency as well as the system impedance estimation error when measuring a 2R1C electrical circuit model. Transthoracic Electrical Bioimpedance (TEB) measurements in a healthy subject were obtained using 3M gel electrodes in a tetrapolar lateral spot electrode configuration. The obtained TEB raw signal was filtered in MATLAB to obtain the respiration and cardiogenic signals, and from the cardiogenic signal the impedance derivative signal (dZ/dt) was also calculated. The results have shown that the maximum continuous impedance estimation rate was approximately 550 measurements per second with a magnitude estimation error below 1% on 2R1C-parallel bridge measurements. The displayed respiration and cardiac signals exhibited good performance, and they could be used to obtain valuable information in some plethysmography monitoring applications. The obtained results suggest that the AD5933-based monitor could be used for the implementation of a portable and wearable Bioimpedance plethysmograph that could be used in applications such as Impedance Cardiography. These results combined with the research done in functional garments and textile electrodes might enable the implementation of PHS applications in a relatively short time from now.

Performance Study of Earth Networks Total Lightning Network using Rocket-Triggered Lightning Data in 2014

NASA Astrophysics Data System (ADS)

Heckman, S.

2015-12-01

Modern lightning locating systems (LLS) provide real-time monitoring and early warning of lightningactivities. In addition, LLS provide valuable data for statistical analysis in lightning research. It isimportant to know the performance of such LLS. In the present study, the performance of the EarthNetworks Total Lightning Network (ENTLN) is studied using rocket-triggered lightning data acquired atthe International Center for Lightning Research and Testing (ICLRT), Camp Blanding, Florida.In the present study, 18 flashes triggered at ICLRT in 2014 were analyzed and they comprise of 78negative cloud-to-ground return strokes. The geometric mean, median, minimum, and maximum for thepeak currents of the 78 return strokes are 13.4 kA, 13.6 kA, 3.7 kA, and 38.4 kA, respectively. The peakcurrents represent typical subsequent return strokes in natural cloud-to-ground lightning.Earth Networks has developed a new data processor to improve the performance of their network. Inthis study, results are presented for the ENTLN data using the old processor (originally reported in 2014)and the ENTLN data simulated using the new processor. The flash detection efficiency, stroke detectionefficiency, percentage of misclassification, median location error, median peak current estimation error,and median absolute peak current estimation error for the originally reported data from old processorare 100%, 94%, 49%, 271 m, 5%, and 13%, respectively, and those for the simulated data using the newprocessor are 100%, 99%, 9%, 280 m, 11%, and 15%, respectively. The use of new processor resulted inhigher stroke detection efficiency and lower percentage of misclassification. It is worth noting that theslight differences in median location error, median peak current estimation error, and median absolutepeak current estimation error for the two processors are due to the fact that the new processordetected more number of return strokes than the old processor.

Retrieving air humidity, global solar radiation, and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part I: humidity

NASA Astrophysics Data System (ADS)

Lobit, P.; López Pérez, L.; Lhomme, J. P.; Gómez Tagle, A.

2017-07-01

This study evaluates the dew point method (Allen et al. 1998) to estimate atmospheric vapor pressure from minimum temperature, and proposes an improved model to estimate it from maximum and minimum temperature. Both methods were evaluated on 786 weather stations in Mexico. The dew point method induced positive bias in dry areas but also negative bias in coastal areas, and its average root mean square error for all evaluated stations was 0.38 kPa. The improved model assumed a bi-linear relation between estimated vapor pressure deficit (difference between saturated vapor pressure at minimum and average temperature) and measured vapor pressure deficit. The parameters of these relations were estimated from historical annual median values of relative humidity. This model removed bias and allowed for a root mean square error of 0.31 kPa. When no historical measurements of relative humidity were available, empirical relations were proposed to estimate it from latitude and altitude, with only a slight degradation on the model accuracy (RMSE = 0.33 kPa, bias = -0.07 kPa). The applicability of the method to other environments is discussed.

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Object recognition and localization from 3D point clouds by maximum-likelihood estimation

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Analytic Perturbation Method for Estimating Ground Flash Fraction from Satellite Lightning Observations

NASA Technical Reports Server (NTRS)

Koshak, William; Solakiewicz, Richard

2013-01-01

An analytic perturbation method is introduced for estimating the lightning ground flash fraction in a set of N lightning flashes observed by a satellite lightning mapper. The value of N is large, typically in the thousands, and the observations consist of the maximum optical group area produced by each flash. The method is tested using simulated observations that are based on Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) data. National Lightning Detection NetworkTM (NLDN) data is used to determine the flash-type (ground or cloud) of the satellite-observed flashes, and provides the ground flash fraction truth for the simulation runs. It is found that the mean ground flash fraction retrieval errors are below 0.04 across the full range 0-1 under certain simulation conditions. In general, it is demonstrated that the retrieval errors depend on many factors (i.e., the number, N, of satellite observations, the magnitude of random and systematic measurement errors, and the number of samples used to form certain climate distributions employed in the model).

Radar and Lidar Radar DEM

NASA Technical Reports Server (NTRS)

Liskovich, Diana; Simard, Marc

2011-01-01

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

PubMed

Huang, Yingbo; Na, Jing; Wu, Xing; Liu, Xiaoqin; Guo, Yu

2015-01-01

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Estimating the population density of the Asian tapir (Tapirus indicus) in a selectively logged forest in Peninsular Malaysia.

PubMed

Rayan, D Mark; Mohamad, Shariff Wan; Dorward, Leejiah; Aziz, Sheema Abdul; Clements, Gopalasamy Reuben; Christopher, Wong Chai Thiam; Traeholt, Carl; Magintan, David

2012-12-01

The endangered Asian tapir (Tapirus indicus) is threatened by large-scale habitat loss, forest fragmentation and increased hunting pressure. Conservation planning for this species, however, is hampered by a severe paucity of information on its ecology and population status. We present the first Asian tapir population density estimate from a camera trapping study targeting tigers in a selectively logged forest within Peninsular Malaysia using a spatially explicit capture-recapture maximum likelihood based framework. With a trap effort of 2496 nights, 17 individuals were identified corresponding to a density (standard error) estimate of 9.49 (2.55) adult tapirs/100 km(2) . Although our results include several caveats, we believe that our density estimate still serves as an important baseline to facilitate the monitoring of tapir population trends in Peninsular Malaysia. Our study also highlights the potential of extracting vital ecological and population information for other cryptic individually identifiable animals from tiger-centric studies, especially with the use of a spatially explicit capture-recapture maximum likelihood based framework. © 2012 Wiley Publishing Asia Pty Ltd, ISZS and IOZ/CAS.

Accuracy of maximum likelihood and least-squares estimates in the lidar slope method with noisy data.

PubMed

Eberhard, Wynn L

2017-04-01

The maximum likelihood estimator (MLE) is derived for retrieving the extinction coefficient and zero-range intercept in the lidar slope method in the presence of random and independent Gaussian noise. Least-squares fitting, weighted by the inverse of the noise variance, is equivalent to the MLE. Monte Carlo simulations demonstrate that two traditional least-squares fitting schemes, which use different weights, are less accurate. Alternative fitting schemes that have some positive attributes are introduced and evaluated. The principal factors governing accuracy of all these schemes are elucidated. Applying these schemes to data with Poisson rather than Gaussian noise alters accuracy little, even when the signal-to-noise ratio is low. Methods to estimate optimum weighting factors in actual data are presented. Even when the weighting estimates are coarse, retrieval accuracy declines only modestly. Mathematical tools are described for predicting retrieval accuracy. Least-squares fitting with inverse variance weighting has optimum accuracy for retrieval of parameters from single-wavelength lidar measurements when noise, errors, and uncertainties are Gaussian distributed, or close to optimum when only approximately Gaussian.

Optimal designs based on the maximum quasi-likelihood estimator

PubMed Central

Shen, Gang; Hyun, Seung Won; Wong, Weng Kee

2016-01-01

We use optimal design theory and construct locally optimal designs based on the maximum quasi-likelihood estimator (MqLE), which is derived under less stringent conditions than those required for the MLE method. We show that the proposed locally optimal designs are asymptotically as efficient as those based on the MLE when the error distribution is from an exponential family, and they perform just as well or better than optimal designs based on any other asymptotically linear unbiased estimators such as the least square estimator (LSE). In addition, we show current algorithms for finding optimal designs can be directly used to find optimal designs based on the MqLE. As an illustrative application, we construct a variety of locally optimal designs based on the MqLE for the 4-parameter logistic (4PL) model and study their robustness properties to misspecifications in the model using asymptotic relative efficiency. The results suggest that optimal designs based on the MqLE can be easily generated and they are quite robust to mis-specification in the probability distribution of the responses. PMID:28163359

Estimation of the Cesium-137 Source Term from the Fukushima Daiichi Power Plant Using Air Concentration and Deposition Data

NASA Astrophysics Data System (ADS)

Winiarek, Victor; Bocquet, Marc; Duhanyan, Nora; Roustan, Yelva; Saunier, Olivier; Mathieu, Anne

2013-04-01

A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativeness of the measurements, the instrumental errors, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, and specially in a situation of sparse observability, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. In Winiarek et al. (2012), we proposed to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We applied the method to the estimation of the Fukushima Daiichi cesium-137 and iodine-131 source terms using activity concentrations in the air. The results were compared to an L-curve estimation technique, and to Desroziers's scheme. Additionally to the estimations of released activities, we provided related uncertainties (12 PBq with a std. of 15 - 20 % for cesium-137 and 190 - 380 PBq with a std. of 5 - 10 % for iodine-131). We also enlightened that, because of the low number of available observations (few hundreds) and even if orders of magnitude were consistent, the reconstructed activities significantly depended on the method used to estimate the prior errors. In order to use more data, we propose to extend the methods to the use of several data types, such as activity concentrations in the air and fallout measurements. The idea is to simultaneously estimate the prior errors related to each dataset, in order to fully exploit the information content of each one. Using the activity concentration measurements, but also daily fallout data from prefectures and cumulated deposition data over a region lying approximately 150 km around the nuclear power plant, we can use a few thousands of data in our inverse modeling algorithm to reconstruct the Cesium-137 source term. To improve the parameterization of removal processes, rainfall fields have also been corrected using outputs from the mesoscale meteorological model WRF and ground station rainfall data. As expected, the different methods yield closer results as the number of data increases. Reference : Winiarek, V., M. Bocquet, O. Saunier, A. Mathieu (2012), Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant : Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant, J. Geophys. Res., 117, D05122, doi:10.1029/2011JD016932.

Skin Friction at Very High Reynolds Numbers in the National Transonic Facility

NASA Technical Reports Server (NTRS)

Watson, Ralph D.; Anders, John B.; Hall, Robert M.

2006-01-01

Skin friction coefficients were derived from measurements using standard measurement technologies on an axisymmetric cylinder in the NASA Langley National Transonic Facility (NTF) at Mach numbers from 0.2 to 0.85. The pressure gradient was nominally zero, the wall temperature was nominally adiabatic, and the ratio of boundary layer thickness to model diameter within the measurement region was 0.10 to 0.14, varying with distance along the model. Reynolds numbers based on momentum thicknesses ranged from 37,000 to 605,000. The measurements approximately doubled the range of available data for flat plate skin friction coefficients. Three different techniques were used to measure surface shear. The maximum error of Preston tube measurements was estimated to be 2.5 percent, while that of Clauser derived measurements was estimated to be approximately 5 percent. Direct measurements by skin friction balance proved to be subject to large errors and were not considered reliable.

Does target viewing time influence perceived reachability?

PubMed

Gabbard, Carl; Ammar, Diala

2007-09-01

This study examined the influence of target viewing time on perceived (estimates of) reachability. Right-handed participants were asked to judge the simulated reachability of midline targets using their dominant limb in viewing conditions of 150 ms, 500 ms, 1 s and 2 s. Responses were compared to actual maximum reach. In reference to percent error, interestingly, the 150 ms condition revealed the least error at peripersonal targets and the most inaccuracy with distal (extrapersonal) targets. This condition was also distinct with a significant overestimation bias -- a common observation in earlier studies. However, with increasing viewing time this bias was reduced. These data provide evidence that 150 ms is effective for estimating reach within one's general peripersonal workspace. However, with judgments distal from that point, more time enhanced accuracy, with 500 ms and 1 s being optimal. Overall results are discussed relative to perceptual effectiveness in programming reaching movements.

Kinematic GPS solutions for aircraft trajectories: Identifying and minimizing systematic height errors associated with atmospheric propagation delays

USGS Publications Warehouse

Shan, S.; Bevis, M.; Kendrick, E.; Mader, G.L.; Raleigh, D.; Hudnut, K.; Sartori, M.; Phillips, D.

2007-01-01

When kinematic GPS processing software is used to estimate the trajectory of an aircraft, unless the delays imposed on the GPS signals by the atmosphere are either estimated or calibrated via external observations, then vertical height errors of decimeters can occur. This problem is clearly manifested when the aircraft is positioned against multiple base stations in areas of pronounced topography because the aircraft height solutions obtained using different base stations will tend to be mutually offset, or biased, in proportion to the elevation differences between the base stations. When performing kinematic surveys in areas with significant topography it should be standard procedure to use multiple base stations, and to separate them vertically to the maximum extent possible, since it will then be much easier to detect mis-modeling of the atmosphere. Copyright 2007 by the American Geophysical Union.

Digital terrestrial photogrammetric methods for tree stem analysis

Treesearch

Neil A. Clark; Randolph H. Wynne; Daniel L. Schmoldt; Matt Winn

2000-01-01

A digital camera was used to measure diameters at various heights along the stem on 20 red oak trees. Diameter at breast height ranged from 16 to over 60 cm, and height to a 10-cm top ranged from 12 to 20 m. The chi-square maximum anticipated error of geometric mean diameter estimates at the 95 percent confidence level was within ±4 cm for all heights when...

Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood

PubMed Central

Bondell, Howard D.; Stefanski, Leonard A.

2013-01-01

Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we develop and study a linear regression estimator that comes close. Efficiency obtains from the estimator’s close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakdown point, and full asymptotic efficiency for normal errors. Simulation evidence shows that compared to existing robust regression estimators, the new estimator has relatively high efficiency for small sample sizes, and comparable outlier resistance. The estimator is further illustrated and compared to existing methods via application to a real data set with purported outliers. PMID:23976805

The early maximum likelihood estimation model of audiovisual integration in speech perception.

PubMed

Andersen, Tobias S

2015-05-01

Speech perception is facilitated by seeing the articulatory mouth movements of the talker. This is due to perceptual audiovisual integration, which also causes the McGurk-MacDonald illusion, and for which a comprehensive computational account is still lacking. Decades of research have largely focused on the fuzzy logical model of perception (FLMP), which provides excellent fits to experimental observations but also has been criticized for being too flexible, post hoc and difficult to interpret. The current study introduces the early maximum likelihood estimation (MLE) model of audiovisual integration to speech perception along with three model variations. In early MLE, integration is based on a continuous internal representation before categorization, which can make the model more parsimonious by imposing constraints that reflect experimental designs. The study also shows that cross-validation can evaluate models of audiovisual integration based on typical data sets taking both goodness-of-fit and model flexibility into account. All models were tested on a published data set previously used for testing the FLMP. Cross-validation favored the early MLE while more conventional error measures favored more complex models. This difference between conventional error measures and cross-validation was found to be indicative of over-fitting in more complex models such as the FLMP.

Shape reconstruction of irregular bodies with multiple complementary data sources

NASA Astrophysics Data System (ADS)

Kaasalainen, M.; Viikinkoski, M.

2012-07-01

We discuss inversion methods for shape reconstruction with complementary data sources. The current main sources are photometry, adaptive optics or other images, occultation timings, and interferometry, and the procedure can readily be extended to include range-Doppler radar and thermal infrared data as well. We introduce the octantoid, a generally applicable shape support that can be automatically used for surface types encountered in planetary research, including strongly nonconvex or non-starlike shapes. We present models of Kleopatra and Hermione from multimodal data as examples of this approach. An important concept in this approach is the optimal weighting of the various data modes. We define the maximum compatibility estimate, a multimodal generalization of the maximum likelihood estimate, for this purpose. We also present a specific version of the procedure for asteroid flyby missions, with which one can reconstruct the complete shape of the target by using the flyby-based map of a part of the surface together with other available data. Finally, we show that the relative volume error of a shape solution is usually approximately equal to the relative shape error rather than its multiple. Our algorithms are trivially parallelizable, so running the code on a CUDA-enabled graphics processing unit is some two orders of magnitude faster than the usual single-processor mode.

Multiple symbol partially coherent detection of MPSK

NASA Technical Reports Server (NTRS)

Simon, M. K.; Divsalar, D.

1992-01-01

It is shown that by using the known (or estimated) value of carrier tracking loop signal to noise ratio (SNR) in the decision metric, it is possible to improve the error probability performance of a partially coherent multiple phase-shift-keying (MPSK) system relative to that corresponding to the commonly used ideal coherent decision rule. Using a maximum-likeihood approach, an optimum decision metric is derived and shown to take the form of a weighted sum of the ideal coherent decision metric (i.e., correlation) and the noncoherent decision metric which is optimum for differential detection of MPSK. The performance of a receiver based on this optimum decision rule is derived and shown to provide continued improvement with increasing length of observation interval (data symbol sequence length). Unfortunately, increasing the observation length does not eliminate the error floor associated with the finite loop SNR. Nevertheless, in the limit of infinite observation length, the average error probability performance approaches the algebraic sum of the error floor and the performance of ideal coherent detection, i.e., at any error probability above the error floor, there is no degradation due to the partial coherence. It is shown that this limiting behavior is virtually achievable with practical size observation lengths. Furthermore, the performance is quite insensitive to mismatch between the estimate of loop SNR (e.g., obtained from measurement) fed to the decision metric and its true value. These results may be of use in low-cost Earth-orbiting or deep-space missions employing coded modulations.

Accuracy and sampling error of two age estimation techniques using rib histomorphometry on a modern sample.

PubMed

García-Donas, Julieta G; Dyke, Jeffrey; Paine, Robert R; Nathena, Despoina; Kranioti, Elena F

2016-02-01

Most age estimation methods are proven problematic when applied in highly fragmented skeletal remains. Rib histomorphometry is advantageous in such cases; yet it is vital to test and revise existing techniques particularly when used in legal settings (Crowder and Rosella, 2007). This study tested Stout & Paine (1992) and Stout et al. (1994) histological age estimation methods on a Modern Greek sample using different sampling sites. Six left 4th ribs of known age and sex were selected from a modern skeletal collection. Each rib was cut into three equal segments. Two thin sections were acquired from each segment. A total of 36 thin sections were prepared and analysed. Four variables (cortical area, intact and fragmented osteon density and osteon population density) were calculated for each section and age was estimated according to Stout & Paine (1992) and Stout et al. (1994). The results showed that both methods produced a systemic underestimation of the individuals (to a maximum of 43 years) although a general improvement in accuracy levels was observed when applying the Stout et al. (1994) formula. There is an increase of error rates with increasing age with the oldest individual showing extreme differences between real age and estimated age. Comparison of the different sampling sites showed small differences between the estimated ages suggesting that any fragment of the rib could be used without introducing significant error. Yet, a larger sample should be used to confirm these results. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Spatial regression test for ensuring temperature data quality in southern Spain

NASA Astrophysics Data System (ADS)

Estévez, J.; Gavilán, P.; García-Marín, A. P.

2018-01-01

Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.

Dentalmaps: Automatic Dental Delineation for Radiotherapy Planning in Head-and-Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thariat, Juliette, E-mail: jthariat@hotmail.com; Ramus, Liliane; INRIA

Purpose: To propose an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, and to assess its accuracy and relevance to guide dental care in the context of intensity-modulated radiotherapy. Methods and Materials: A multi-atlas-based segmentation, less sensitive to artifacts than previously published head-and-neck segmentation methods, was used. The manual segmentations of a 21-patient database were first deformed onto the query using nonlinear registrations with the training images and then fused to estimate the consensus segmentation of the query. Results: The framework was evaluated with a leave-one-out protocol. The maximum doses estimated using manual contours were considered as groundmore » truth and compared with the maximum doses estimated using automatic contours. The dose estimation error was within 2-Gy accuracy in 75% of cases (with a median of 0.9 Gy), whereas it was within 2-Gy accuracy in 30% of cases only with the visual estimation method without any contour, which is the routine practice procedure. Conclusions: Dose estimates using this framework were more accurate than visual estimates without dental contour. Dentalmaps represents a useful documentation and communication tool between radiation oncologists and dentists in routine practice. Prospective multicenter assessment is underway on patients extrinsic to the database.« less

Underwater passive acoustic localization of Pacific walruses in the northeastern Chukchi Sea.

PubMed

Rideout, Brendan P; Dosso, Stan E; Hannay, David E

2013-09-01

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.

Conditional estimation using prior information in 2-stage group sequential designs assuming asymptotic normality when the trial terminated early.

PubMed

Shimura, Masashi; Maruo, Kazushi; Gosho, Masahiko

2018-04-23

Two-stage designs are widely used to determine whether a clinical trial should be terminated early. In such trials, a maximum likelihood estimate is often adopted to describe the difference in efficacy between the experimental and reference treatments; however, this method is known to display conditional bias. To reduce such bias, a conditional mean-adjusted estimator (CMAE) has been proposed, although the remaining bias may be nonnegligible when a trial is stopped for efficacy at the interim analysis. We propose a new estimator for adjusting the conditional bias of the treatment effect by extending the idea of the CMAE. This estimator is calculated by weighting the maximum likelihood estimate obtained at the interim analysis and the effect size prespecified when calculating the sample size. We evaluate the performance of the proposed estimator through analytical and simulation studies in various settings in which a trial is stopped for efficacy or futility at the interim analysis. We find that the conditional bias of the proposed estimator is smaller than that of the CMAE when the information time at the interim analysis is small. In addition, the mean-squared error of the proposed estimator is also smaller than that of the CMAE. In conclusion, we recommend the use of the proposed estimator for trials that are terminated early for efficacy or futility. Copyright © 2018 John Wiley & Sons, Ltd.

A Comparative Analysis of Three Monocular Passive Ranging Methods on Real Infrared Sequences

NASA Astrophysics Data System (ADS)

Bondžulić, Boban P.; Mitrović, Srđan T.; Barbarić, Žarko P.; Andrić, Milenko S.

2013-09-01

Three monocular passive ranging methods are analyzed and tested on the real infrared sequences. The first method exploits scale changes of an object in successive frames, while other two use Beer-Lambert's Law. Ranging methods are evaluated by comparing with simultaneously obtained reference data at the test site. Research is addressed on scenarios where multiple sensor views or active measurements are not possible. The results show that these methods for range estimation can provide the fidelity required for object tracking. Maximum values of relative distance estimation errors in near-ideal conditions are less than 8%.

Estimation of brood and nest survival: Comparative methods in the presence of heterogeneity

USGS Publications Warehouse

Manly, Bryan F.J.; Schmutz, Joel A.

2001-01-01

The Mayfield method has been widely used for estimating survival of nests and young animals, especially when data are collected at irregular observation intervals. However, this method assumes survival is constant throughout the study period, which often ignores biologically relevant variation and may lead to biased survival estimates. We examined the bias and accuracy of 1 modification to the Mayfield method that allows for temporal variation in survival, and we developed and similarly tested 2 additional methods. One of these 2 new methods is simply an iterative extension of Klett and Johnson's method, which we refer to as the Iterative Mayfield method and bears similarity to Kaplan-Meier methods. The other method uses maximum likelihood techniques for estimation and is best applied to survival of animals in groups or families, rather than as independent individuals. We also examined how robust these estimators are to heterogeneity in the data, which can arise from such sources as dependent survival probabilities among siblings, inherent differences among families, and adoption. Testing of estimator performance with respect to bias, accuracy, and heterogeneity was done using simulations that mimicked a study of survival of emperor goose (Chen canagica) goslings. Assuming constant survival for inappropriately long periods of time or use of Klett and Johnson's methods resulted in large bias or poor accuracy (often >5% bias or root mean square error) compared to our Iterative Mayfield or maximum likelihood methods. Overall, estimator performance was slightly better with our Iterative Mayfield than our maximum likelihood method, but the maximum likelihood method provides a more rigorous framework for testing covariates and explicity models a heterogeneity factor. We demonstrated use of all estimators with data from emperor goose goslings. We advocate that future studies use the new methods outlined here rather than the traditional Mayfield method or its previous modifications.

Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Casertano, Stefano

1991-01-01

A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

Using weighted power mean for equivalent square estimation.

PubMed

Zhou, Sumin; Wu, Qiuwen; Li, Xiaobo; Ma, Rongtao; Zheng, Dandan; Wang, Shuo; Zhang, Mutian; Li, Sicong; Lei, Yu; Fan, Qiyong; Hyun, Megan; Diener, Tyler; Enke, Charles

2017-11-01

Equivalent Square (ES) enables the calculation of many radiation quantities for rectangular treatment fields, based only on measurements from square fields. While it is widely applied in radiotherapy, its accuracy, especially for extremely elongated fields, still leaves room for improvement. In this study, we introduce a novel explicit ES formula based on Weighted Power Mean (WPM) function and compare its performance with the Sterling formula and Vadash/Bjärngard's formula. The proposed WPM formula is ESWPMa,b=waα+1-wbα1/α for a rectangular photon field with sides a and b. The formula performance was evaluated by three methods: standard deviation of model fitting residual error, maximum relative model prediction error, and model's Akaike Information Criterion (AIC). Testing datasets included the ES table from British Journal of Radiology (BJR), photon output factors (S cp ) from the Varian TrueBeam Representative Beam Data (Med Phys. 2012;39:6981-7018), and published S cp data for Varian TrueBeam Edge (J Appl Clin Med Phys. 2015;16:125-148). For the BJR dataset, the best-fit parameter value α = -1.25 achieved a 20% reduction in standard deviation in ES estimation residual error compared with the two established formulae. For the two Varian datasets, employing WPM reduced the maximum relative error from 3.5% (Sterling) or 2% (Vadash/Bjärngard) to 0.7% for open field sizes ranging from 3 cm to 40 cm, and the reduction was even more prominent for 1 cm field sizes on Edge (J Appl Clin Med Phys. 2015;16:125-148). The AIC value of the WPM formula was consistently lower than its counterparts from the traditional formulae on photon output factors, most prominent on very elongated small fields. The WPM formula outperformed the traditional formulae on three testing datasets. With increasing utilization of very elongated, small rectangular fields in modern radiotherapy, improved photon output factor estimation is expected by adopting the WPM formula in treatment planning and secondary MU check. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Depth-of-interaction estimates in pixelated scintillator sensors using Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Sharma, Diksha; Sze, Christina; Bhandari, Harish; Nagarkar, Vivek; Badano, Aldo

2017-01-01

Image quality in thick scintillator detectors can be improved by minimizing parallax errors through depth-of-interaction (DOI) estimation. A novel sensor for low-energy single photon imaging having a thick, transparent, crystalline pixelated micro-columnar CsI:Tl scintillator structure has been described, with possible future application in small-animal single photon emission computed tomography (SPECT) imaging when using thicker structures under development. In order to understand the fundamental limits of this new structure, we introduce cartesianDETECT2, an open-source optical transport package that uses Monte Carlo methods to obtain estimates of DOI for improving spatial resolution of nuclear imaging applications. Optical photon paths are calculated as a function of varying simulation parameters such as columnar surface roughness, bulk, and top-surface absorption. We use scanning electron microscope images to estimate appropriate surface roughness coefficients. Simulation results are analyzed to model and establish patterns between DOI and photon scattering. The effect of varying starting locations of optical photons on the spatial response is studied. Bulk and top-surface absorption fractions were varied to investigate their effect on spatial response as a function of DOI. We investigated the accuracy of our DOI estimation model for a particular screen with various training and testing sets, and for all cases the percent error between the estimated and actual DOI over the majority of the detector thickness was ±5% with a maximum error of up to ±10% at deeper DOIs. In addition, we found that cartesianDETECT2 is computationally five times more efficient than MANTIS. Findings indicate that DOI estimates can be extracted from a double-Gaussian model of the detector response. We observed that our model predicts DOI in pixelated scintillator detectors reasonably well.

Temporally diffeomorphic cardiac motion estimation from three-dimensional echocardiography by minimization of intensity consistency error.

PubMed

Zhang, Zhijun; Ashraf, Muhammad; Sahn, David J; Song, Xubo

2014-05-01

Quantitative analysis of cardiac motion is important for evaluation of heart function. Three dimensional (3D) echocardiography is among the most frequently used imaging modalities for motion estimation because it is convenient, real-time, low-cost, and nonionizing. However, motion estimation from 3D echocardiographic sequences is still a challenging problem due to low image quality and image corruption by noise and artifacts. The authors have developed a temporally diffeomorphic motion estimation approach in which the velocity field instead of the displacement field was optimized. The optimal velocity field optimizes a novel similarity function, which we call the intensity consistency error, defined as multiple consecutive frames evolving to each time point. The optimization problem is solved by using the steepest descent method. Experiments with simulated datasets, images of anex vivo rabbit phantom, images of in vivo open-chest pig hearts, and healthy human images were used to validate the authors' method. Simulated and real cardiac sequences tests showed that results in the authors' method are more accurate than other competing temporal diffeomorphic methods. Tests with sonomicrometry showed that the tracked crystal positions have good agreement with ground truth and the authors' method has higher accuracy than the temporal diffeomorphic free-form deformation (TDFFD) method. Validation with an open-access human cardiac dataset showed that the authors' method has smaller feature tracking errors than both TDFFD and frame-to-frame methods. The authors proposed a diffeomorphic motion estimation method with temporal smoothness by constraining the velocity field to have maximum local intensity consistency within multiple consecutive frames. The estimated motion using the authors' method has good temporal consistency and is more accurate than other temporally diffeomorphic motion estimation methods.

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

NASA Astrophysics Data System (ADS)

Xiong, Qiufen; Hu, Jianglin

2013-05-01

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and daily temporal scale. The primary factors influencing the dataset precision are elevation and terrain complexity. In general, the gridded dataset has a relatively high precision in plains and flatlands and a relatively low precision in mountainous areas.

Performance Analysis of HF Band FB-MC-SS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hussein Moradi; Stephen Andrew Laraway; Behrouz Farhang-Boroujeny

Abstract—In a recent paper [1] the filter bank multicarrier spread spectrum (FB-MC-SS) waveform was proposed for wideband spread spectrum HF communications. A significant benefit of this waveform is robustness against narrow and partial band interference. Simulation results in [1] demonstrated good performance in a wideband HF channel over a wide range of conditions. In this paper we present a theoretical analysis of the bit error probably for this system. Our analysis tailors the results from [2] where BER performance was analyzed for maximum ration combining systems that accounted for correlation between subcarriers and channel estimation error. Equations are give formore » BER that closely match the simulated performance in most situations.« less

Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas.

PubMed

Colgan, Matthew S; Asner, Gregory P; Swemmer, Tony

2013-07-01

Tree biomass is an integrated measure of net growth and is critical for understanding, monitoring, and modeling ecosystem functions. Despite the importance of accurately measuring tree biomass, several fundamental barriers preclude direct measurement at large spatial scales, including the facts that trees must be felled to be weighed and that even modestly sized trees are challenging to maneuver once felled. Allometric methods allow for estimation of tree mass using structural characteristics, such as trunk diameter. Savanna trees present additional challenges, including limited available allometry and a prevalence of multiple stems per individual. Here we collected airborne lidar data over a semiarid savanna adjacent to the Kruger National Park, South Africa, and then harvested and weighed woody plant biomass at the plot scale to provide a standard against which field and airborne estimation methods could be compared. For an existing airborne lidar method, we found that half of the total error was due to averaging canopy height at the plot scale. This error was eliminated by instead measuring maximum height and crown area of individual trees from lidar data using an object-based method to identify individual tree crowns and estimate their biomass. The best object-based model approached the accuracy of field allometry at both the tree and plot levels, and it more than doubled the accuracy compared to existing airborne methods (17% vs. 44% deviation from harvested biomass). Allometric error accounted for less than one-third of the total residual error in airborne biomass estimates at the plot scale when using allometry with low bias. Airborne methods also gave more accurate predictions at the plot level than did field methods based on diameter-only allometry. These results provide a novel comparison of field and airborne biomass estimates using harvested plots and advance the role of lidar remote sensing in savanna ecosystems.

Comparison of irrigation pumpage with change in ground-water storage in the High Plains aquifer in Chase, Dundy, and Perkins counties, Nebraska, 1975-83

USGS Publications Warehouse

Heimes, F.J.; Ferrigno, C.F.; Gutentag, E.D.; Lucky, R.R.; Stephens, D.M.; Weeks, J.B.

1987-01-01

The relation between pumpage and change in storage was evaluated for most of a three-county area in southwestern Nebraska from 1975 through 1983. Initial comparison of the 1975-83 pumpage with change in storage in the study area indicated that the 1 ,042,300 acre-ft of change in storage was only about 30% of the 3,425,000 acre-ft of pumpage. An evaluation of the data used to calculate pumpage and change in storage indicated that there was a relatively large potential for error in estimates of specific yield. As a result, minimum and maximum values of specific yield were estimated and used to recalculate change in storage. Estimates also were derived for the minimum and maximum amounts of recharge that could occur as a result of cultivation practices. The minimum and maximum estimates for specific yield and for recharge from cultivation practices were used to compute a range of values for the potential amount of additional recharge that occurred as a result of irrigation. The minimum and maximum amounts of recharge that could be caused by irrigation in the study area were 953,200 acre-ft (28% of pumpage) and 2,611,200 acre-ft (76% of pumpage), respectively. These values indicate that a substantial percentage of the water pumped from the aquifer is resupplied to storage in the aquifer as a result of a combination of irrigation return flow and enhanced recharge from precipitation that results from cultivation and irrigation practices. (Author 's abstract)

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

Maximum likelihood method for estimating airplane stability and control parameters from flight data in frequency domain

NASA Technical Reports Server (NTRS)

Klein, V.

1980-01-01

A frequency domain maximum likelihood method is developed for the estimation of airplane stability and control parameters from measured data. The model of an airplane is represented by a discrete-type steady state Kalman filter with time variables replaced by their Fourier series expansions. The likelihood function of innovations is formulated, and by its maximization with respect to unknown parameters the estimation algorithm is obtained. This algorithm is then simplified to the output error estimation method with the data in the form of transformed time histories, frequency response curves, or spectral and cross-spectral densities. The development is followed by a discussion on the equivalence of the cost function in the time and frequency domains, and on advantages and disadvantages of the frequency domain approach. The algorithm developed is applied in four examples to the estimation of longitudinal parameters of a general aviation airplane using computer generated and measured data in turbulent and still air. The cost functions in the time and frequency domains are shown to be equivalent; therefore, both approaches are complementary and not contradictory. Despite some computational advantages of parameter estimation in the frequency domain, this approach is limited to linear equations of motion with constant coefficients.

Probabilistic models in human sensorimotor control

PubMed Central

Wolpert, Daniel M.

2009-01-01

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty. PMID:17628731

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

NASA Astrophysics Data System (ADS)

Thron, Christopher; Tran, Khoi; Smith, Douglas; Benincasa, Daniel

2017-05-01

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users' WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system's performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Accuracy and optimal timing of activity measurements in estimating the absorbed dose of radioiodine in the treatment of Graves' disease

NASA Astrophysics Data System (ADS)

Merrill, S.; Horowitz, J.; Traino, A. C.; Chipkin, S. R.; Hollot, C. V.; Chait, Y.

2011-02-01

Calculation of the therapeutic activity of radioiodine 131I for individualized dosimetry in the treatment of Graves' disease requires an accurate estimate of the thyroid absorbed radiation dose based on a tracer activity administration of 131I. Common approaches (Marinelli-Quimby formula, MIRD algorithm) use, respectively, the effective half-life of radioiodine in the thyroid and the time-integrated activity. Many physicians perform one, two, or at most three tracer dose activity measurements at various times and calculate the required therapeutic activity by ad hoc methods. In this paper, we study the accuracy of estimates of four 'target variables': time-integrated activity coefficient, time of maximum activity, maximum activity, and effective half-life in the gland. Clinical data from 41 patients who underwent 131I therapy for Graves' disease at the University Hospital in Pisa, Italy, are used for analysis. The radioiodine kinetics are described using a nonlinear mixed-effects model. The distributions of the target variables in the patient population are characterized. Using minimum root mean squared error as the criterion, optimal 1-, 2-, and 3-point sampling schedules are determined for estimation of the target variables, and probabilistic bounds are given for the errors under the optimal times. An algorithm is developed for computing the optimal 1-, 2-, and 3-point sampling schedules for the target variables. This algorithm is implemented in a freely available software tool. Taking into consideration 131I effective half-life in the thyroid and measurement noise, the optimal 1-point time for time-integrated activity coefficient is a measurement 1 week following the tracer dose. Additional measurements give only a slight improvement in accuracy.

Estimation of River Bathymetry from ATI-SAR Data

NASA Astrophysics Data System (ADS)

Almeida, T. G.; Walker, D. T.; Farquharson, G.

2013-12-01

A framework for estimation of river bathymetry from surface velocity observation data is presented using variational inverse modeling applied to the 2D depth-averaged, shallow-water equations (SWEs) including bottom friction. We start with with a cost function defined by the error between observed and estimated surface velocities, and introduce the SWEs as a constraint on the velocity field. The constrained minimization problem is converted to an unconstrained minimization through the use of Lagrange multipliers, and an adjoint SWE model is developed. The adjoint model solution is used to calculate the gradient of the cost function with respect to river bathymetry. The gradient is used in a descent algorithm to determine the bathymetry that yields a surface velocity field that is a best-fit to the observational data. In applying the algorithm, the 2D depth-averaged flow is computed assuming a known, constant discharge rate and a known, uniform bottom-friction coefficient; a correlation relating surface velocity and depth-averaged velocity is also used. Observation data was collected using a dual beam squinted along-track-interferometric, synthetic-aperture radar (ATI-SAR) system, which provides two independent components of the surface velocity, oriented roughly 30 degrees fore and aft of broadside, offering high-resolution bank-to-bank velocity vector coverage of the river. Data and bathymetry estimation results are presented for two rivers, the Snohomish River near Everett, WA and the upper Sacramento River, north of Colusa, CA. The algorithm results are compared to available measured bathymetry data, with favorable results. General trends show that the water-depth estimates are most accurate in shallow regions, and performance is sensitive to the accuracy of the specified discharge rate and bottom friction coefficient. The results also indicate that, for a given reach, the estimated water depth reaches a maximum that is smaller than the true depth; this apparent maximum depth scales with the true river depth and discharge rate, so that the deepest parts of the river show the largest bathymetry errors.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model

PubMed Central

Starke, Ludger; Ostwald, Dirk

2017-01-01

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation. PMID:28966572

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

USGS Publications Warehouse

Chen, Mingshi; Senay, Gabriel B.; Singh, Ramesh K.; Verdin, James P.

2016-01-01

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (ETo); and parameters, differential temperature (dT), and maximum ET scalar (Kmax), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (ETo and LST) and two key parameters (Kmax and dT).

Uncertainty quantification of CO₂ saturation estimated from electrical resistance tomography data at the Cranfield site

DOE PAGES

Yang, Xianjin; Chen, Xiao; Carrigan, Charles R.; ...

2014-06-03

A parametric bootstrap approach is presented for uncertainty quantification (UQ) of CO₂ saturation derived from electrical resistance tomography (ERT) data collected at the Cranfield, Mississippi (USA) carbon sequestration site. There are many sources of uncertainty in ERT-derived CO₂ saturation, but we focus on how the ERT observation errors propagate to the estimated CO₂ saturation in a nonlinear inversion process. Our UQ approach consists of three steps. We first estimated the observational errors from a large number of reciprocal ERT measurements. The second step was to invert the pre-injection baseline data and the resulting resistivity tomograph was used as the priormore » information for nonlinear inversion of time-lapse data. We assigned a 3% random noise to the baseline model. Finally, we used a parametric bootstrap method to obtain bootstrap CO₂ saturation samples by deterministically solving a nonlinear inverse problem many times with resampled data and resampled baseline models. Then the mean and standard deviation of CO₂ saturation were calculated from the bootstrap samples. We found that the maximum standard deviation of CO₂ saturation was around 6% with a corresponding maximum saturation of 30% for a data set collected 100 days after injection began. There was no apparent spatial correlation between the mean and standard deviation of CO₂ saturation but the standard deviation values increased with time as the saturation increased. The uncertainty in CO₂ saturation also depends on the ERT reciprocal error threshold used to identify and remove noisy data and inversion constraints such as temporal roughness. Five hundred realizations requiring 3.5 h on a single 12-core node were needed for the nonlinear Monte Carlo inversion to arrive at stationary variances while the Markov Chain Monte Carlo (MCMC) stochastic inverse approach may expend days for a global search. This indicates that UQ of 2D or 3D ERT inverse problems can be performed on a laptop or desktop PC.« less

On the angular variation of thermal infrared emissivity of inorganic soils

NASA Astrophysics Data System (ADS)

GarcíA-Santos, Vicente; Valor, Enric; Caselles, Vicente; ÁNgeles Burgos, M.; Coll, CéSar

2012-10-01

Land surface temperature (LST), a key parameter for many environmental studies, can be most readily estimated by using thermal infrared (TIR) sensors onboard satellites. Accurate LST are contingent upon simultaneously accurate estimates of land surface emissivity (ɛ), which depend on sensor viewing angle and the anisotropy of optical and structural properties of surfaces. In the case of inorganic bare soils (IBS), there are still few data that quantify emissivity angular effects. The present work deals with the angular variation of TIR emissivity for twelve IBS types, representative of nine of the twelve soil textures found on Earth according to United States Department of Agriculture classification. Emissivity was measured with a maximum error of ±0.01, in several spectral ranges within the atmospheric window 7.7-14.3 μm, at different zenithal (θ) and azimuthal (φ) angles. Results showed that ɛ of all IBS studied is almost azimuthally isotropic, and also zenithally up to θ = 40°, from which ɛ values decrease with the increase of θ. This decrease is most pronounced in sandy IBS which is rich in quartz reaching a maximum difference from nadir of +0.101 at θ = 70°. On the other hand, clayey IBS did not show a significant decrease of ɛ up to θ= 60°. A parameterization of the relative-to-nadir emissivity in terms ofθ and sand and clay percentage was established. Finally, the impact of ignoring ɛangular effects on the retrievals of LST, using split-window-type algorithms, and of outgoing longwave radiation, was analyzed. Results showed systematic errors ranging between ±0.4 K to ±1.3 K for atmospheres with water vapor values lower than 4 cm in the case of LST, and errors between 2%-8%, in the estimation of different terms of the surface energy balance.

Estimating residual fault hitting rates by recapture sampling

NASA Technical Reports Server (NTRS)

Lee, Larry; Gupta, Rajan

1988-01-01

For the recapture debugging design introduced by Nayak (1988) the problem of estimating the hitting rates of the faults remaining in the system is considered. In the context of a conditional likelihood, moment estimators are derived and are shown to be asymptotically normal and fully efficient. Fixed sample properties of the moment estimators are compared, through simulation, with those of the conditional maximum likelihood estimators. Properties of the conditional model are investigated such as the asymptotic distribution of linear functions of the fault hitting frequencies and a representation of the full data vector in terms of a sequence of independent random vectors. It is assumed that the residual hitting rates follow a log linear rate model and that the testing process is truncated when the gaps between the detection of new errors exceed a fixed amount of time.

Inference from single occasion capture experiments using genetic markers.

PubMed

Hettiarachchige, Chathurika K H; Huggins, Richard M

2018-05-01

Accurate estimation of the size of animal populations is an important task in ecological science. Recent advances in the field of molecular genetics researches allow the use of genetic data to estimate the size of a population from a single capture occasion rather than repeated occasions as in the usual capture-recapture experiments. Estimating the population size using genetic data also has sometimes led to estimates that differ markedly from each other and also from classical capture-recapture estimates. Here, we develop a closed form estimator that uses genetic information to estimate the size of a population consisting of mothers and daughters, focusing on estimating the number of mothers, using data from a single sample. We demonstrate the estimator is consistent and propose a parametric bootstrap to estimate the standard errors. The estimator is evaluated in a simulation study and applied to real data. We also consider maximum likelihood in this setting and discover problems that preclude its general use. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Approximate maximum likelihood decoding of block codes

NASA Technical Reports Server (NTRS)

Greenberger, H. J.

1979-01-01

Approximate maximum likelihood decoding algorithms, based upon selecting a small set of candidate code words with the aid of the estimated probability of error of each received symbol, can give performance close to optimum with a reasonable amount of computation. By combining the best features of various algorithms and taking care to perform each step as efficiently as possible, a decoding scheme was developed which can decode codes which have better performance than those presently in use and yet not require an unreasonable amount of computation. The discussion of the details and tradeoffs of presently known efficient optimum and near optimum decoding algorithms leads, naturally, to the one which embodies the best features of all of them.

A model for the statistical description of analytical errors occurring in clinical chemical laboratories with time.

PubMed

Hyvärinen, A

1985-01-01

The main purpose of the present study was to describe the statistical behaviour of daily analytical errors in the dimensions of place and time, providing a statistical basis for realistic estimates of the analytical error, and hence allowing the importance of the error and the relative contributions of its different sources to be re-evaluated. The observation material consists of creatinine and glucose results for control sera measured in daily routine quality control in five laboratories for a period of one year. The observation data were processed and computed by means of an automated data processing system. Graphic representations of time series of daily observations, as well as their means and dispersion limits when grouped over various time intervals, were investigated. For partition of the total variation several two-way analyses of variance were done with laboratory and various time classifications as factors. Pooled sets of observations were tested for normality of distribution and for consistency of variances, and the distribution characteristics of error variation in different categories of place and time were compared. Errors were found from the time series to vary typically between days. Due to irregular fluctuations in general and particular seasonal effects in creatinine, stable estimates of means or of dispersions for errors in individual laboratories could not be easily obtained over short periods of time but only from data sets pooled over long intervals (preferably at least one year). Pooled estimates of proportions of intralaboratory variation were relatively low (less than 33%) when the variation was pooled within days. However, when the variation was pooled over longer intervals this proportion increased considerably, even to a maximum of 89-98% (95-98% in each method category) when an outlying laboratory in glucose was omitted, with a concomitant decrease in the interaction component (representing laboratory-dependent variation with time). This indicates that a substantial part of the variation comes from intralaboratory variation with time rather than from constant interlaboratory differences. Normality and consistency of statistical distributions were best achieved in the long-term intralaboratory sets of the data, under which conditions the statistical estimates of error variability were also most characteristic of the individual laboratories rather than necessarily being similar to one another. Mixing of data from different laboratories may give heterogeneous and nonparametric distributions and hence is not advisable.(ABSTRACT TRUNCATED AT 400 WORDS)

Statistical inference with quantum measurements: methodologies for nitrogen vacancy centers in diamond

NASA Astrophysics Data System (ADS)

Hincks, Ian; Granade, Christopher; Cory, David G.

2018-01-01

The analysis of photon count data from the standard nitrogen vacancy (NV) measurement process is treated as a statistical inference problem. This has applications toward gaining better and more rigorous error bars for tasks such as parameter estimation (e.g. magnetometry), tomography, and randomized benchmarking. We start by providing a summary of the standard phenomenological model of the NV optical process in terms of Lindblad jump operators. This model is used to derive random variables describing emitted photons during measurement, to which finite visibility, dark counts, and imperfect state preparation are added. NV spin-state measurement is then stated as an abstract statistical inference problem consisting of an underlying biased coin obstructed by three Poisson rates. Relevant frequentist and Bayesian estimators are provided, discussed, and quantitatively compared. We show numerically that the risk of the maximum likelihood estimator is well approximated by the Cramér-Rao bound, for which we provide a simple formula. Of the estimators, we in particular promote the Bayes estimator, owing to its slightly better risk performance, and straightforward error propagation into more complex experiments. This is illustrated on experimental data, where quantum Hamiltonian learning is performed and cross-validated in a fully Bayesian setting, and compared to a more traditional weighted least squares fit.

Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

NASA Astrophysics Data System (ADS)

Elmasri, B.; Rahman, A. F.

2010-12-01

Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation will result in improved GPP predictions. Although there might be a room for improvements in our model outcomes through improved parameterization, our results suggest that such a methodology for running BIOME-BGC model based entirely on routinely available data can produce good predictions of GPP.

A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

NASA Astrophysics Data System (ADS)

Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

2015-12-01

The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

Information matrix estimation procedures for cognitive diagnostic models.

PubMed

Liu, Yanlou; Xin, Tao; Andersson, Björn; Tian, Wei

2018-03-06

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance. © 2018 The British Psychological Society.

Quantitative, Comparable Coherent Anti-Stokes Raman Scattering (CARS) Spectroscopy: Correcting Errors in Phase Retrieval

PubMed Central

Camp, Charles H.; Lee, Young Jong; Cicerone, Marcus T.

2017-01-01

Coherent anti-Stokes Raman scattering (CARS) microspectroscopy has demonstrated significant potential for biological and materials imaging. To date, however, the primary mechanism of disseminating CARS spectroscopic information is through pseudocolor imagery, which explicitly neglects a vast majority of the hyperspectral data. Furthermore, current paradigms in CARS spectral processing do not lend themselves to quantitative sample-to-sample comparability. The primary limitation stems from the need to accurately measure the so-called nonresonant background (NRB) that is used to extract the chemically-sensitive Raman information from the raw spectra. Measurement of the NRB on a pixel-by-pixel basis is a nontrivial task; thus, reference NRB from glass or water are typically utilized, resulting in error between the actual and estimated amplitude and phase. In this manuscript, we present a new methodology for extracting the Raman spectral features that significantly suppresses these errors through phase detrending and scaling. Classic methods of error-correction, such as baseline detrending, are demonstrated to be inaccurate and to simply mask the underlying errors. The theoretical justification is presented by re-developing the theory of phase retrieval via the Kramers-Kronig relation, and we demonstrate that these results are also applicable to maximum entropy method-based phase retrieval. This new error-correction approach is experimentally applied to glycerol spectra and tissue images, demonstrating marked consistency between spectra obtained using different NRB estimates, and between spectra obtained on different instruments. Additionally, in order to facilitate implementation of these approaches, we have made many of the tools described herein available free for download. PMID:28819335

Dependence of Aerosol Light Absorption and Single-Scattering Albedo On Ambient Relative Humidity for Sulfate Aerosols with Black Carbon Cores

NASA Technical Reports Server (NTRS)

Redemann, Jens; Russell, Philip B.; Hamill, Patrick

2001-01-01

Atmospheric aerosols frequently contain hygroscopic sulfate species and black carbon (soot) inclusions. In this paper we report results of a modeling study to determine the change in aerosol absorption due to increases in ambient relative humidity (RH), for three common sulfate species, assuming that the soot mass fraction is present as a single concentric core within each particle. Because of the lack of detailed knowledge about various input parameters to models describing internally mixed aerosol particle optics, we focus on results that were aimed at determining the maximum effect that particle humidification may have on aerosol light absorption. In the wavelength range from 450 to 750 nm, maximum absorption humidification factors (ratio of wet to 'dry=30% RH' absorption) for single aerosol particles are found to be as large as 1.75 when the RH changes from 30 to 99.5%. Upon lesser humidification from 30 to 80% RH, absorption humidification for single particles is only as much as 1.2, even for the most favorable combination of initial ('dry') soot mass fraction and particle size. Integrated over monomodal lognormal particle size distributions, maximum absorption humidification factors range between 1.07 and 1.15 for humidification from 30 to 80% and between 1.1 and 1.35 for humidification from 30 to 95% RH for all species considered. The largest humidification factors at a wavelength of 450 nm are obtained for 'dry' particle size distributions that peak at a radius of 0.05 microns, while the absorption humidification factors at 700 nm are largest for 'dry' size distributions that are dominated by particles in the radius range of 0.06 to 0.08 microns. Single-scattering albedo estimates at ambient conditions are often based on absorption measurements at low RH (approx. 30%) and the assumption that aerosol absorption does not change upon humidification (i.e., absorption humidification equal to unity). Our modeling study suggests that this assumption alone can introduce absolute errors in estimates of the midvisible single-scattering albedo of up to 0.05 for realistic dry particle size distributions. Our study also indicates that this error increases with increasing wavelength. The potential errors in aerosol single-scattering albedo derived here are comparable in magnitude and in addition to uncertainties in single-scattering albedo estimates that are based on measurements of aerosol light absorption and scattering.

Parameter recovery, bias and standard errors in the linear ballistic accumulator model.

PubMed

Visser, Ingmar; Poessé, Rens

2017-05-01

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model. © 2017 The British Psychological Society.

MIMO equalization with adaptive step size for few-mode fiber transmission systems.

PubMed

van Uden, Roy G H; Okonkwo, Chigo M; Sleiffer, Vincent A J M; de Waardt, Hugo; Koonen, Antonius M J

2014-01-13

Optical multiple-input multiple-output (MIMO) transmission systems generally employ minimum mean squared error time or frequency domain equalizers. Using an experimental 3-mode dual polarization coherent transmission setup, we show that the convergence time of the MMSE time domain equalizer (TDE) and frequency domain equalizer (FDE) can be reduced by approximately 50% and 30%, respectively. The criterion used to estimate the system convergence time is the time it takes for the MIMO equalizer to reach an average output error which is within a margin of 5% of the average output error after 50,000 symbols. The convergence reduction difference between the TDE and FDE is attributed to the limited maximum step size for stable convergence of the frequency domain equalizer. The adaptive step size requires a small overhead in the form of a lookup table. It is highlighted that the convergence time reduction is achieved without sacrificing optical signal-to-noise ratio performance.

An Assessment of State-of-the-Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

NASA Astrophysics Data System (ADS)

Skourup, Henriette; Farrell, Sinéad Louise; Hendricks, Stefan; Ricker, Robert; Armitage, Thomas W. K.; Ridout, Andy; Andersen, Ole Baltazar; Haas, Christian; Baker, Steven

2017-11-01

State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high-frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along-track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03-0.06 m.

Adaptive Offset Correction for Intracortical Brain Computer Interfaces

PubMed Central

Homer, Mark L.; Perge, János A.; Black, Michael J.; Harrison, Matthew T.; Cash, Sydney S.; Hochberg, Leigh R.

2014-01-01

Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ±10.1%; p<0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs. PMID:24196868

Adaptive offset correction for intracortical brain-computer interfaces.

PubMed

Homer, Mark L; Perge, Janos A; Black, Michael J; Harrison, Matthew T; Cash, Sydney S; Hochberg, Leigh R

2014-03-01

Intracortical brain-computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user's ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called multiple offset correction algorithm (MOCA), was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors ( 10.6 ± 10.1% ; p < 0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.

An analysis of carrier phase jitter in an MPSK receiver utilizing map estimation. Ph.D. Thesis Semiannual Status Report, Jul. 1993 - Jan. 1994

NASA Technical Reports Server (NTRS)

Osborne, William P.

1994-01-01

The use of 8 and 16 PSK TCM to support satellite communications in an effort to achieve more bandwidth efficiency in a power-limited channel has been proposed. This project addresses the problem of carrier phase jitter in an M-PSK receiver utilizing the high SNR approximation to the maximum aposteriori estimation of carrier phase. In particular, numerical solutions to the 8 and 16 PSK self-noise and phase detector gain in the carrier tracking loop are presented. The effect of changing SNR on the loop noise bandwidth is also discussed. These data are then used to compute variance of phase error as a function of SNR. Simulation and hardware data are used to verify these calculations. The results show that there is a threshold in the variance of phase error versus SNR curves that is a strong function of SNR and a weak function of loop bandwidth. The M-PSK variance thresholds occur at SNR's in the range of practical interest for the use of 8 and 16-PSK TCM. This suggests that phase error variance is an important consideration in the design of these systems.

Influence of Joint Angle on EMG-Torque Model During Constant-Posture, Torque-Varying Contractions.

PubMed

Liu, Pu; Liu, Lukai; Clancy, Edward A

2015-11-01

Relating the electromyogram (EMG) to joint torque is useful in various application areas, including prosthesis control, ergonomics and clinical biomechanics. Limited study has related EMG to torque across varied joint angles, particularly when subjects performed force-varying contractions or when optimized modeling methods were utilized. We related the biceps-triceps surface EMG of 22 subjects to elbow torque at six joint angles (spanning 60° to 135°) during constant-posture, torque-varying contractions. Three nonlinear EMG σ -torque models, advanced EMG amplitude (EMG σ ) estimation processors (i.e., whitened, multiple-channel) and the duration of data used to train models were investigated. When EMG-torque models were formed separately for each of the six distinct joint angles, a minimum "gold standard" error of 4.01±1.2% MVC(F90) resulted (i.e., error relative to maximum voluntary contraction at 90° flexion). This model structure, however, did not directly facilitate interpolation across angles. The best model which did so achieved a statistically equivalent error of 4.06±1.2% MVC(F90). Results demonstrated that advanced EMG σ processors lead to improved joint torque estimation as do longer model training durations.

Estimation of maximum tolerated dose for long-term bioassays from acute lethal dose and structure by QSAR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gombar, V.K.; Enslein, K.; Hart, J.B.

1991-09-01

A quantitative structure-activity relationship (QSAR) model has been developed to estimate maximum tolerated doses (MTD) from structural features of chemicals and the corresponding oral acute lethal doses (LD50) as determined in male rats. The model is based on a set of 269 diverse chemicals which have been tested under the National Cancer Institute/National Toxicology Program (NCI/NTP) protocols. The rat oral LD50 value was the strongest predictor. Additionally, 22 structural descriptors comprising nine substructural MOLSTAC(c) keys, three molecular connectivity indices, and sigma charges on 10 molecular fragments were identified as endpoint predictors. The model explains 76% of the variance and ismore » significant (F = 35.7) at p less than 0.0001 with a standard error of the estimate of 0.40 in the log (1/mol) units used in Hansch-type equations. Cross-validation showed that the difference between the average deleted residual square (0.179) and the model residual square (0.160) was not significant (t = 0.98).« less

A Parametric k-Means Algorithm

PubMed Central

Tarpey, Thaddeus

2007-01-01

Summary The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points of a parametric distribution. Cluster means from the k-means algorithm are nonparametric estimators of principal points. A parametric k-means approach is introduced for estimating principal points by running the k-means algorithm on a very large simulated data set from a distribution whose parameters are estimated using maximum likelihood. Theoretical and simulation results are presented comparing the parametric k-means algorithm to the usual k-means algorithm and an example on determining sizes of gas masks is used to illustrate the parametric k-means algorithm. PMID:17917692

Parameter estimation of an ARMA model for river flow forecasting using goal programming

NASA Astrophysics Data System (ADS)

Mohammadi, Kourosh; Eslami, H. R.; Kahawita, Rene

2006-11-01

SummaryRiver flow forecasting constitutes one of the most important applications in hydrology. Several methods have been developed for this purpose and one of the most famous techniques is the Auto regressive moving average (ARMA) model. In the research reported here, the goal was to minimize the error for a specific season of the year as well as for the complete series. Goal programming (GP) was used to estimate the ARMA model parameters. Shaloo Bridge station on the Karun River with 68 years of observed stream flow data was selected to evaluate the performance of the proposed method. The results when compared with the usual method of maximum likelihood estimation were favorable with respect to the new proposed algorithm.

The effect of tropospheric fluctuations on the accuracy of water vapor radiometry

NASA Technical Reports Server (NTRS)

Wilcox, J. Z.

1992-01-01

Line-of-sight path delay calibration accuracies of 1 mm are needed to improve both angular and Doppler tracking capabilities. Fluctuations in the refractivity of tropospheric water vapor limit the present accuracies to about 1 nrad for the angular position and to a delay rate of 3x10(exp -13) sec/sec over a 100-sec time interval for Doppler tracking. This article describes progress in evaluating the limitations of the technique of water vapor radiometry at the 1-mm level. The two effects evaluated here are: (1) errors arising from tip-curve calibration of WVR's in the presence of tropospheric fluctuations and (2) errors due to the use of nonzero beamwidths for water vapor radiometer (WVR) horns. The error caused by tropospheric water vapor fluctuations during instrument calibration from a single tip curve is 0.26 percent in the estimated gain for a tip-curve duration of several minutes or less. This gain error causes a 3-mm bias and a 1-mm scale factor error in the estimated path delay at a 10-deg elevation per 1 g/cm(sup 2) of zenith water vapor column density present in the troposphere during the astrometric observation. The error caused by WVR beam averaging of tropospheric fluctuations is 3 mm at a 10-deg elevation per 1 g/cm(sup 2) of zenith water vapor (and is proportionally higher for higher water vapor content) for current WVR beamwidths (full width at half maximum of approximately 6 deg). This is a stochastic error (which cannot be calibrated) and which can be reduced to about half of its instantaneous value by time averaging the radio signal over several minutes. The results presented here suggest two improvements to WVR design: first, the gain of the instruments should be stabilized to 4 parts in 10(exp 4) over a calibration period lasting 5 hours, and second, the WVR antenna beamwidth should be reduced to about 0.2 deg. This will reduce the error induced by water vapor fluctuations in the estimated path delays to less than 1 mm for the elevation range from zenith to 6 deg for most observation weather conditions.

An adaptive modeling and simulation environment for combined-cycle data reconciliation and degradation estimation

NASA Astrophysics Data System (ADS)

Lin, Tsungpo

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principal component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.

Estimating discharge in rivers using remotely sensed hydraulic information

USGS Publications Warehouse

Bjerklie, D.M.; Moller, D.; Smith, L.C.; Dingman, S.L.

2005-01-01

A methodology to estimate in-bank river discharge exclusively from remotely sensed hydraulic data is developed. Water-surface width and maximum channel width measured from 26 aerial and digital orthophotos of 17 single channel rivers and 41 SAR images of three braided rivers were coupled with channel slope data obtained from topographic maps to estimate the discharge. The standard error of the discharge estimates were within a factor of 1.5-2 (50-100%) of the observed, with the mean estimate accuracy within 10%. This level of accuracy was achieved using calibration functions developed from observed discharge. The calibration functions use reach specific geomorphic variables, the maximum channel width and the channel slope, to predict a correction factor. The calibration functions are related to channel type. Surface velocity and width information, obtained from a single C-band image obtained by the Jet Propulsion Laboratory's (JPL's) AirSAR was also used to estimate discharge for a reach of the Missouri River. Without using a calibration function, the estimate accuracy was +72% of the observed discharge, which is within the expected range of uncertainty for the method. However, using the observed velocity to calibrate the initial estimate improved the estimate accuracy to within +10% of the observed. Remotely sensed discharge estimates with accuracies reported in this paper could be useful for regional or continental scale hydrologic studies, or in regions where ground-based data is lacking. ?? 2004 Elsevier B.V. All rights reserved.

An assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue, Ireland

NASA Astrophysics Data System (ADS)

Harrington, Seán T.; Harrington, Joseph R.

2013-03-01

This paper presents an assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue in Ireland. The rivers, located in the South of Ireland, are underlain by sandstone, limestones and mudstones, and the catchments are primarily agricultural. A comprehensive database of suspended sediment data is not available for rivers in Ireland. For such situations, it is common to estimate suspended sediment concentrations from the flow rate using the suspended sediment rating curve approach. These rating curves are most commonly constructed by applying linear regression to the logarithms of flow and suspended sediment concentration or by applying a power curve to normal data. Both methods are assessed in this paper for the Rivers Bandon and Owenabue. Turbidity-based suspended sediment loads are presented for each river based on continuous (15 min) flow data and the use of turbidity as a surrogate for suspended sediment concentration is investigated. A database of paired flow rate and suspended sediment concentration values, collected between the years 2004 and 2011, is used to generate rating curves for each river. From these, suspended sediment load estimates using the rating curve approach are estimated and compared to the turbidity based loads for each river. Loads are also estimated using stage and seasonally separated rating curves and daily flow data, for comparison purposes. The most accurate load estimate on the River Bandon is found using a stage separated power curve, while the most accurate load estimate on the River Owenabue is found using a general power curve. Maximum full monthly errors of - 76% to + 63% are found on the River Bandon with errors of - 65% to + 359% found on the River Owenabue. The average monthly error on the River Bandon is - 12% with an average error of + 87% on the River Owenabue. The use of daily flow data in the load estimation process does not result in a significant loss of accuracy on either river. Historic load estimates (with a 95% confidence interval) were hindcast from the flow record and average annual loads of 7253 ± 673 tonnes on the River Bandon and 1935 ± 325 tonnes on the River Owenabue were estimated to be passing the gauging stations.

Aerosol lidar observations of atmospheric mixing in Los Angeles: Climatology and implications for greenhouse gas observations

NASA Astrophysics Data System (ADS)

Ware, John; Kort, Eric A.; DeCola, Phil; Duren, Riley

2016-08-01

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport—especially vertical mixing—is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day-to-day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL's portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing.

Aerosol lidar observations of atmospheric mixing in Los Angeles: Climatology and implications for greenhouse gas observations.

PubMed

Ware, John; Kort, Eric A; DeCola, Phil; Duren, Riley

2016-08-27

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport-especially vertical mixing-is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day-to-day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL's portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing.

Analysis of vestibular schwannoma size in multiple dimensions: a comparative cohort study of different measurement techniques.

PubMed

Varughese, J K; Wentzel-Larsen, T; Vassbotn, F; Moen, G; Lund-Johansen, M

2010-04-01

In this volumetric study of the vestibular schwannoma, we evaluated the accuracy and reliability of several approximation methods that are in use, and determined the minimum volume difference that needs to be measured for it to be attributable to an actual difference rather than a retest error. We also found empirical proportionality coefficients for the different methods. DESIGN/SETTING AND PARTICIPANTS: Methodological study with investigation of three different VS measurement methods compared to a reference method that was based on serial slice volume estimates. These volume estimates were based on: (i) one single diameter, (ii) three orthogonal diameters or (iii) the maximal slice area. Altogether 252 T1-weighted MRI images with gadolinium contrast, from 139 VS patients, were examined. The retest errors, in terms of relative percentages, were determined by undertaking repeated measurements on 63 scans for each method. Intraclass correlation coefficients were used to assess the agreement between each of the approximation methods and the reference method. The tendency for approximation methods to systematically overestimate/underestimate different-sized tumours was also assessed, with the help of Bland-Altman plots. The most commonly used approximation method, the maximum diameter, was the least reliable measurement method and has inherent weaknesses that need to be considered. This includes greater retest errors than area-based measurements (25% and 15%, respectively), and that it was the only approximation method that could not easily be converted into volumetric units. Area-based measurements can furthermore be more reliable for smaller volume differences than diameter-based measurements. All our findings suggest that the maximum diameter should not be used as an approximation method. We propose the use of measurement modalities that take into account growth in multiple dimensions instead.

Kernel Wiener filter and its application to pattern recognition.

PubMed

Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

2010-11-01

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

Mathematical model to interpret localized reflectance spectra measured in the presence of a strong fluorescence marker

NASA Astrophysics Data System (ADS)

Bravo, Jaime J.; Davis, Scott C.; Roberts, David W.; Paulsen, Keith D.; Kanick, Stephen C.

2016-06-01

Quantification of multiple fluorescence markers during neurosurgery has the potential to provide complementary contrast mechanisms between normal and malignant tissues, and one potential combination involves fluorescein sodium (FS) and aminolevulinic acid-induced protoporphyrin IX (PpIX). We focus on the interpretation of reflectance spectra containing contributions from elastically scattered (reflected) photons as well as fluorescence emissions from a strong fluorophore (i.e., FS). A model-based approach to extract μa and μs‧ in the presence of FS emission is validated in optical phantoms constructed with Intralipid (1% to 2% lipid) and whole blood (1% to 3% volume fraction), over a wide range of FS concentrations (0 to 1000 μg/ml). The results show that modeling reflectance as a combination of elastically scattered light and attenuation-corrected FS-based emission yielded more accurate tissue parameter estimates when compared with a nonmodified reflectance model, with reduced maximum errors for blood volume (22% versus 90%), microvascular saturation (21% versus 100%), and μs‧ (13% versus 207%). Additionally, quantitative PpIX fluorescence sampled in the same phantom as FS showed significant differences depending on the reflectance model used to estimate optical properties (i.e., maximum error 29% versus 86%). These data represent a first step toward using quantitative optical spectroscopy to guide surgeries through simultaneous assessment of FS and PpIX.

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

NASA Technical Reports Server (NTRS)

Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

2014-01-01

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

Rigorous covariance propagation of geoid errors to geodetic MDT estimates

NASA Astrophysics Data System (ADS)

Pail, R.; Albertella, A.; Fecher, T.; Savcenko, R.

2012-04-01

The mean dynamic topography (MDT) is defined as the difference between the mean sea surface (MSS) derived from satellite altimetry, averaged over several years, and the static geoid. Assuming geostrophic conditions, from the MDT the ocean surface velocities as important component of global ocean circulation can be derived from it. Due to the availability of GOCE gravity field models, for the very first time MDT can now be derived solely from satellite observations (altimetry and gravity) down to spatial length-scales of 100 km and even below. Global gravity field models, parameterized in terms of spherical harmonic coefficients, are complemented by the full variance-covariance matrix (VCM). Therefore, for the geoid component a realistic statistical error estimate is available, while the error description of the altimetric component is still an open issue and is, if at all, attacked empirically. In this study we make the attempt to perform, based on the full gravity VCM, rigorous error propagation to derived geostrophic surface velocities, thus also considering all correlations. For the definition of the static geoid we use the third release of the time-wise GOCE model, as well as the satellite-only combination model GOCO03S. In detail, we will investigate the velocity errors resulting from the geoid component in dependence of the harmonic degree, and the impact of using/no using covariances on the MDT errors and its correlations. When deriving an MDT, it is spectrally filtered to a certain maximum degree, which is usually driven by the signal content of the geoid model, by applying isotropic or non-isotropic filters. Since this filtering is acting also on the geoid component, the consistent integration of this filter process into the covariance propagation shall be performed, and its impact shall be quantified. The study will be performed for MDT estimates in specific test areas of particular oceanographic interest.

Error-Rate Bounds for Coded PPM on a Poisson Channel

NASA Technical Reports Server (NTRS)

Moision, Bruce; Hamkins, Jon

2009-01-01

Equations for computing tight bounds on error rates for coded pulse-position modulation (PPM) on a Poisson channel at high signal-to-noise ratio have been derived. These equations and elements of the underlying theory are expected to be especially useful in designing codes for PPM optical communication systems. The equations and the underlying theory apply, more specifically, to a case in which a) At the transmitter, a linear outer code is concatenated with an inner code that includes an accumulator and a bit-to-PPM-symbol mapping (see figure) [this concatenation is known in the art as "accumulate-PPM" (abbreviated "APPM")]; b) The transmitted signal propagates on a memoryless binary-input Poisson channel; and c) At the receiver, near-maximum-likelihood (ML) decoding is effected through an iterative process. Such a coding/modulation/decoding scheme is a variation on the concept of turbo codes, which have complex structures, such that an exact analytical expression for the performance of a particular code is intractable. However, techniques for accurately estimating the performances of turbo codes have been developed. The performance of a typical turbo code includes (1) a "waterfall" region consisting of a steep decrease of error rate with increasing signal-to-noise ratio (SNR) at low to moderate SNR, and (2) an "error floor" region with a less steep decrease of error rate with increasing SNR at moderate to high SNR. The techniques used heretofore for estimating performance in the waterfall region have differed from those used for estimating performance in the error-floor region. For coded PPM, prior to the present derivations, equations for accurate prediction of the performance of coded PPM at high SNR did not exist, so that it was necessary to resort to time-consuming simulations in order to make such predictions. The present derivation makes it unnecessary to perform such time-consuming simulations.

Joint Transmitter and Receiver Power Allocation under Minimax MSE Criterion with Perfect and Imperfect CSI for MC-CDMA Transmissions

NASA Astrophysics Data System (ADS)

Kotchasarn, Chirawat; Saengudomlert, Poompat

We investigate the problem of joint transmitter and receiver power allocation with the minimax mean square error (MSE) criterion for uplink transmissions in a multi-carrier code division multiple access (MC-CDMA) system. The objective of power allocation is to minimize the maximum MSE among all users each of which has limited transmit power. This problem is a nonlinear optimization problem. Using the Lagrange multiplier method, we derive the Karush-Kuhn-Tucker (KKT) conditions which are necessary for a power allocation to be optimal. Numerical results indicate that, compared to the minimum total MSE criterion, the minimax MSE criterion yields a higher total MSE but provides a fairer treatment across the users. The advantages of the minimax MSE criterion are more evident when we consider the bit error rate (BER) estimates. Numerical results show that the minimax MSE criterion yields a lower maximum BER and a lower average BER. We also observe that, with the minimax MSE criterion, some users do not transmit at full power. For comparison, with the minimum total MSE criterion, all users transmit at full power. In addition, we investigate robust joint transmitter and receiver power allocation where the channel state information (CSI) is not perfect. The CSI error is assumed to be unknown but bounded by a deterministic value. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results show that, with imperfect CSI, the minimax MSE criterion also outperforms the minimum total MSE criterion in terms of the maximum and average BERs.

Comparison of three-dimensional parameters of Halo CMEs using three cone models

NASA Astrophysics Data System (ADS)

Na, H.; Moon, Y.; Jang, S.; Lee, K.

2012-12-01

Halo coronal mass ejections (HCMEs) are a major cause of geomagnetic storms and their three dimensional structures are important for space weather. In this study, we compare three cone models: an elliptical cone model, an ice-cream cone model, and an asymmetric cone model. These models allow us to determine the three dimensional parameters of HCMEs such as radial speed, angular width, and the angle (γ) between sky plane and cone axis. We compare these parameters obtained from three models using 62 well-observed HCMEs observed by SOHO/LASCO from 2001 to 2002. Then we obtain the root mean square error (RMS error) between maximum measured projection speeds and their calculated projection speeds from the cone models. As a result, we find that the radial speeds obtained from the models are well correlated with one another (R > 0.84). The correlation coefficients between angular widths are ranges from 0.04 to 0.53 and those between γ values are from -0.15 to 0.47, which are much smaller than expected. The reason may be due to different assumptions and methods. The RMS errors between the maximum measured projection speeds and the maximum estimated projection speeds of the elliptical cone model, the ice-cream cone model, and the asymmetric cone model are 213 km/s, 254 km/s, and 267 km/s, respectively. And we obtain the correlation coefficients between the location from the models and the flare location (R > 0.75). Finally, we discuss strengths and weaknesses of these models in terms of space weather application.

Nonparametric Discrete Survival Function Estimation with Uncertain Endpoints Using an Internal Validation Subsample

PubMed Central

Zee, Jarcy; Xie, Sharon X.

2015-01-01

Summary When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop an estimated likelihood function for the situation where we have both uncertain endpoints for all participants and true endpoints for only a subset of participants. We propose a nonparametric maximum estimated likelihood estimator of the discrete survival function of time to the true endpoint. We show that the proposed estimator is consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias compared to the naïve Kaplan-Meier survival function estimator, which uses only uncertain endpoints, and more efficient with moderate missingness compared to the complete-case Kaplan-Meier survival function estimator, which uses only available true endpoints. Finally, we apply the proposed method to a dataset for estimating the risk of developing Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative. PMID:25916510

Modeling Water Temperature in the Yakima River, Washington, from Roza Diversion Dam to Prosser Dam, 2005-06

USGS Publications Warehouse

Voss, Frank D.; Curran, Christopher A.; Mastin, Mark C.

2008-01-01

A mechanistic water-temperature model was constructed by the U.S. Geological Survey for use by the Bureau of Reclamation for studying the effect of potential water management decisions on water temperature in the Yakima River between Roza and Prosser, Washington. Flow and water temperature data for model input were obtained from the Bureau of Reclamation Hydromet database and from measurements collected by the U.S. Geological Survey during field trips in autumn 2005. Shading data for the model were collected by the U.S. Geological Survey in autumn 2006. The model was calibrated with data collected from April 1 through October 31, 2005, and tested with data collected from April 1 through October 31, 2006. Sensitivity analysis results showed that for the parameters tested, daily maximum water temperature was most sensitive to changes in air temperature and solar radiation. Root mean squared error for the five sites used for model calibration ranged from 1.3 to 1.9 degrees Celsius (?C) and mean error ranged from ?1.3 to 1.6?C. The root mean squared error for the five sites used for testing simulation ranged from 1.6 to 2.2?C and mean error ranged from 0.1 to 1.3?C. The accuracy of the stream temperatures estimated by the model is limited by four errors (model error, data error, parameter error, and user error).

Estimation of end point foot clearance points from inertial sensor data.

PubMed

Santhiranayagam, Braveena K; Lai, Daniel T H; Begg, Rezaul K; Palaniswami, Marimuthu

2011-01-01

Foot clearance parameters provide useful insight into tripping risks during walking. This paper proposes a technique for the estimate of key foot clearance parameters using inertial sensor (accelerometers and gyroscopes) data. Fifteen features were extracted from raw inertial sensor measurements, and a regression model was used to estimate two key foot clearance parameters: First maximum vertical clearance (m x 1) after toe-off and the Minimum Toe Clearance (MTC) of the swing foot. Comparisons are made against measurements obtained using an optoelectronic motion capture system (Optotrak), at 4 different walking speeds. General Regression Neural Networks (GRNN) were used to estimate the desired parameters from the sensor features. Eight subjects foot clearance data were examined and a Leave-one-subject-out (LOSO) method was used to select the best model. The best average Root Mean Square Errors (RMSE) across all subjects obtained using all sensor features at the maximum speed for m x 1 was 5.32 mm and for MTC was 4.04 mm. Further application of a hill-climbing feature selection technique resulted in 0.54-21.93% improvement in RMSE and required fewer input features. The results demonstrated that using raw inertial sensor data with regression models and feature selection could accurately estimate key foot clearance parameters.

Methods for estimating magnitude and frequency of floods in Montana based on data through 1983

USGS Publications Warehouse

Omang, R.J.; Parrett, Charles; Hull, J.A.

1986-01-01

Equations are presented for estimating flood magnitudes for ungaged sites in Montana based on data through 1983. The State was divided into eight regions based on hydrologic conditions, and separate multiple regression equations were developed for each region. These equations relate annual flood magnitudes and frequencies to basin characteristics and are applicable only to natural flow streams. In three of the regions, equations also were developed relating flood magnitudes and frequencies to basin characteristics and channel geometry measurements. The standard errors of estimate for an exceedance probability of 1% ranged from 39% to 87%. Techniques are described for estimating annual flood magnitude and flood frequency information at ungaged sites based on data from gaged sites on the same stream. Included are curves relating flood frequency information to drainage area for eight major streams in the State. Maximum known flood magnitudes in Montana are compared with estimated 1 %-chance flood magnitudes and with maximum known floods in the United States. Values of flood magnitudes for selected exceedance probabilities and values of significant basin characteristics and channel geometry measurements for all gaging stations used in the analysis are tabulated. Included are 375 stations in Montana and 28 nearby stations in Canada and adjoining States. (Author 's abstract)

Diurnal variations of the Martian surface layer meteorological parameters during the first 45 sols at two Viking Lander sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, J.L.; Leovy, C.B.; Tillman, J.E.

1978-12-01

Wind speed, ambient and surface temperatures from both Viking Landers have been used to compute bulk Richardson numbers and Monin-Obukhov lengths during the earliest phase of the Mars missions. These parameters are used to estimate drag and heat transfer coefficients, friction velocities and surface heat fluxes at the two sites. The principal uncertainty is in the specification of the roughness length. Maximum heat fluxes occur near local noon at both sites, and are estimated to be in the range 15--20 W m/sup -2/ at the Viking 1 site and 10--15 W m/sup -2/ at the Viking 2 site. Maximum valuesmore » of friction velocity occur in late morning at Viking 1 and are estimated to be 0.4--0.6 m s/sup -1/. They occur shortly after drawn at the Viking 2 site where peak values are estimated to be in the range 0.25--0.35 m s/sup -1/. Extension of these calculations to later times during the mission will require allowance for dust opacity effects in the estimation of surface temperature and in the correction of radiation errors of the Viking 2 temperature sensor.« less

Estimating linear-nonlinear models using Rényi divergences

PubMed Central

Kouh, Minjoon; Sharpee, Tatyana O.

2009-01-01

This paper compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramér-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data. PMID:19568981

Estimating linear-nonlinear models using Renyi divergences.

PubMed

Kouh, Minjoon; Sharpee, Tatyana O

2009-01-01

This article compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramer-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data.

Self-calibration method without joint iteration for distributed small satellite SAR systems

NASA Astrophysics Data System (ADS)

Xu, Qing; Liao, Guisheng; Liu, Aifei; Zhang, Juan

2013-12-01

The performance of distributed small satellite synthetic aperture radar systems degrades significantly due to the unavoidable array errors, including gain, phase, and position errors, in real operating scenarios. In the conventional method proposed in (IEEE T Aero. Elec. Sys. 42:436-451, 2006), the spectrum components within one Doppler bin are considered as calibration sources. However, it is found in this article that the gain error estimation and the position error estimation in the conventional method can interact with each other. The conventional method may converge to suboptimal solutions in large position errors since it requires the joint iteration between gain-phase error estimation and position error estimation. In addition, it is also found that phase errors can be estimated well regardless of position errors when the zero Doppler bin is chosen. In this article, we propose a method obtained by modifying the conventional one, based on these two observations. In this modified method, gain errors are firstly estimated and compensated, which eliminates the interaction between gain error estimation and position error estimation. Then, by using the zero Doppler bin data, the phase error estimation can be performed well independent of position errors. Finally, position errors are estimated based on the Taylor-series expansion. Meanwhile, the joint iteration between gain-phase error estimation and position error estimation is not required. Therefore, the problem of suboptimal convergence, which occurs in the conventional method, can be avoided with low computational method. The modified method has merits of faster convergence and lower estimation error compared to the conventional one. Theoretical analysis and computer simulation results verified the effectiveness of the modified method.

Predicting vertical jump height from bar velocity.

PubMed

García-Ramos, Amador; Štirn, Igor; Padial, Paulino; Argüelles-Cienfuegos, Javier; De la Fuente, Blanca; Strojnik, Vojko; Feriche, Belén

2015-06-01

The objective of the study was to assess the use of maximum (Vmax) and final propulsive phase (FPV) bar velocity to predict jump height in the weighted jump squat. FPV was defined as the velocity reached just before bar acceleration was lower than gravity (-9.81 m·s(-2)). Vertical jump height was calculated from the take-off velocity (Vtake-off) provided by a force platform. Thirty swimmers belonging to the National Slovenian swimming team performed a jump squat incremental loading test, lifting 25%, 50%, 75% and 100% of body weight in a Smith machine. Jump performance was simultaneously monitored using an AMTI portable force platform and a linear velocity transducer attached to the barbell. Simple linear regression was used to estimate jump height from the Vmax and FPV recorded by the linear velocity transducer. Vmax (y = 16.577x - 16.384) was able to explain 93% of jump height variance with a standard error of the estimate of 1.47 cm. FPV (y = 12.828x - 6.504) was able to explain 91% of jump height variance with a standard error of the estimate of 1.66 cm. Despite that both variables resulted to be good predictors, heteroscedasticity in the differences between FPV and Vtake-off was observed (r(2) = 0.307), while the differences between Vmax and Vtake-off were homogenously distributed (r(2) = 0.071). These results suggest that Vmax is a valid tool for estimating vertical jump height in a loaded jump squat test performed in a Smith machine. Key pointsVertical jump height in the loaded jump squat can be estimated with acceptable precision from the maximum bar velocity recorded by a linear velocity transducer.The relationship between the point at which bar acceleration is less than -9.81 m·s(-2) and the real take-off is affected by the velocity of movement.Mean propulsive velocity recorded by a linear velocity transducer does not appear to be optimal to monitor ballistic exercise performance.

Predicting Vertical Jump Height from Bar Velocity

PubMed Central

García-Ramos, Amador; Štirn, Igor; Padial, Paulino; Argüelles-Cienfuegos, Javier; De la Fuente, Blanca; Strojnik, Vojko; Feriche, Belén

2015-01-01

The objective of the study was to assess the use of maximum (Vmax) and final propulsive phase (FPV) bar velocity to predict jump height in the weighted jump squat. FPV was defined as the velocity reached just before bar acceleration was lower than gravity (-9.81 m·s-2). Vertical jump height was calculated from the take-off velocity (Vtake-off) provided by a force platform. Thirty swimmers belonging to the National Slovenian swimming team performed a jump squat incremental loading test, lifting 25%, 50%, 75% and 100% of body weight in a Smith machine. Jump performance was simultaneously monitored using an AMTI portable force platform and a linear velocity transducer attached to the barbell. Simple linear regression was used to estimate jump height from the Vmax and FPV recorded by the linear velocity transducer. Vmax (y = 16.577x - 16.384) was able to explain 93% of jump height variance with a standard error of the estimate of 1.47 cm. FPV (y = 12.828x - 6.504) was able to explain 91% of jump height variance with a standard error of the estimate of 1.66 cm. Despite that both variables resulted to be good predictors, heteroscedasticity in the differences between FPV and Vtake-off was observed (r2 = 0.307), while the differences between Vmax and Vtake-off were homogenously distributed (r2 = 0.071). These results suggest that Vmax is a valid tool for estimating vertical jump height in a loaded jump squat test performed in a Smith machine. Key points Vertical jump height in the loaded jump squat can be estimated with acceptable precision from the maximum bar velocity recorded by a linear velocity transducer. The relationship between the point at which bar acceleration is less than -9.81 m·s-2 and the real take-off is affected by the velocity of movement. Mean propulsive velocity recorded by a linear velocity transducer does not appear to be optimal to monitor ballistic exercise performance. PMID:25983572

Analysis of an all-digital maximum likelihood carrier phase and clock timing synchronizer for eight phase-shift keying modulation

NASA Astrophysics Data System (ADS)

Degaudenzi, Riccardo; Vanghi, Vieri

1994-02-01

In all-digital Trellis-Coded 8PSK (TC-8PSK) demodulator well suited for VLSI implementation, including maximum likelihood estimation decision-directed (MLE-DD) carrier phase and clock timing recovery, is introduced and analyzed. By simply removing the trellis decoder the demodulator can efficiently cope with uncoded 8PSK signals. The proposed MLE-DD synchronization algorithm requires one sample for the phase and two samples per symbol for the timing loop. The joint phase and timing discriminator characteristics are analytically derived and numerical results checked by means of computer simulations. An approximated expression for steady-state carrier phase and clock timing mean square error has been derived and successfully checked with simulation findings. Synchronizer deviation from the Cramer Rao bound is also discussed. Mean acquisition time for the digital synchronizer has also been computed and checked, using the Monte Carlo simulation technique. Finally, TC-8PSK digital demodulator performance in terms of bit error rate and mean time to lose lock, including digital interpolators and synchronization loops, is presented.

Design, construction and performance evaluation of the target tissue thickness measurement system in intraoperative radiotherapy for breast cancer

NASA Astrophysics Data System (ADS)

Yazdani, Mohammad Reza; Setayeshi, Saeed; Arabalibeik, Hossein; Akbari, Mohammad Esmaeil

2017-05-01

Intraoperative electron radiation therapy (IOERT), which uses electron beams for irradiating the target directly during the surgery, has the advantage of delivering a homogeneous dose to a controlled layer of tissue. Since the dose falls off quickly below the target thickness, the underlying normal tissues are spared. In selecting the appropriate electron energy, the accuracy of the target tissue thickness measurement is critical. In contrast to other procedures applied in IOERT, the routine measurement method is considered to be completely traditional and approximate. In this work, a novel mechanism is proposed for measuring the target tissue thickness with an acceptable level of accuracy. An electronic system has been designed and manufactured with the capability of measuring the tissue thickness based on the recorded electron density under the target. The results indicated the possibility of thickness measurement with a maximum error of 2 mm for 91.35% of data. Aside from system limitation in estimating the thickness of 5 mm phantom, for 88.94% of data, maximum error is 1 mm.

Profile-likelihood Confidence Intervals in Item Response Theory Models.

PubMed

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

Multi crop area estimation in Idaho using EDITOR

NASA Technical Reports Server (NTRS)

Sheffner, E. J.

1984-01-01

The use of LANDSAT multispectral scanner digital data for multi-crop acreage estimation in the central Snake River Plain of Idaho was examined. Two acquisitions of LANDSAT data covering ground sample units selected from a U.S. Department of Agriculture sampling frame in a four country study site were used to train a maximum likelihood classifier which, subsequently, classified all picture elements in the study site. Acreage estimates for six major crops, by county and for the four counties combined, were generated from the classification using the Battesse-Fuller model for estimation by regression in small areas. Results from the regression analysis were compared to those obtained by direct expansion of the ground data. Using the LANDSAT data significantly decreased the errors associated with the estimates for the three largest acreage crops. The late date of the second LANDSAT acquisition may have contributed to the poor results for three summer crops.

Radar modulation classification using time-frequency representation and nonlinear regression

NASA Astrophysics Data System (ADS)

De Luigi, Christophe; Arques, Pierre-Yves; Lopez, Jean-Marc; Moreau, Eric

1999-09-01

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

On-Orbit Multi-Field Wavefront Control with a Kalman Filter

NASA Technical Reports Server (NTRS)

Lou, John; Sigrist, Norbert; Basinger, Scott; Redding, David

2008-01-01

A document describes a multi-field wavefront control (WFC) procedure for the James Webb Space Telescope (JWST) on-orbit optical telescope element (OTE) fine-phasing using wavefront measurements at the NIRCam pupil. The control is applied to JWST primary mirror (PM) segments and secondary mirror (SM) simultaneously with a carefully selected ordering. Through computer simulations, the multi-field WFC procedure shows that it can reduce the initial system wavefront error (WFE), as caused by random initial system misalignments within the JWST fine-phasing error budget, from a few dozen micrometers to below 50 nm across the entire NIRCam Field of View, and the WFC procedure is also computationally stable as the Monte-Carlo simulations indicate. With the incorporation of a Kalman Filter (KF) as an optical state estimator into the WFC process, the robustness of the JWST OTE alignment process can be further improved. In the presence of some large optical misalignments, the Kalman state estimator can provide a reasonable estimate of the optical state, especially for those degrees of freedom that have a significant impact on the system WFE. The state estimate allows for a few corrections to the optical state to push the system towards its nominal state, and the result is that a large part of the WFE can be eliminated in this step. When the multi-field WFC procedure is applied after Kalman state estimate and correction, the stability of fine-phasing control is much more certain. Kalman Filter has been successfully applied to diverse applications as a robust and optimal state estimator. In the context of space-based optical system alignment based on wavefront measurements, a KF state estimator can combine all available wavefront measurements, past and present, as well as measurement and actuation error statistics to generate a Maximum-Likelihood optimal state estimator. The strength and flexibility of the KF algorithm make it attractive for use in real-time optical system alignment when WFC alone cannot effectively align the system.

Improving estimates of genetic maps: a meta-analysis-based approach.

PubMed

Stewart, William C L

2007-07-01

Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps. Copyright 2007 Wiley-Liss, Inc.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Thickness distribution of a cooling pyroclastic flow deposit on Augustine Volcano, Alaska: Optimization using InSAR, FEMs, and an adaptive mesh algorithm

USGS Publications Warehouse

Masterlark, Timothy; Lu, Zhong; Rykhus, Russell P.

2006-01-01

Interferometric synthetic aperture radar (InSAR) imagery documents the consistent subsidence, during the interval 1992–1999, of a pyroclastic flow deposit (PFD) emplaced during the 1986 eruption of Augustine Volcano, Alaska. We construct finite element models (FEMs) that simulate thermoelastic contraction of the PFD to account for the observed subsidence. Three-dimensional problem domains of the FEMs include a thermoelastic PFD embedded in an elastic substrate. The thickness of the PFD is initially determined from the difference between post- and pre-eruption digital elevation models (DEMs). The initial excess temperature of the PFD at the time of deposition, 640 °C, is estimated from FEM predictions and an InSAR image via standard least-squares inverse methods. Although the FEM predicts the major features of the observed transient deformation, systematic prediction errors (RMSE = 2.2 cm) are most likely associated with errors in the a priori PFD thickness distribution estimated from the DEM differences. We combine an InSAR image, FEMs, and an adaptive mesh algorithm to iteratively optimize the geometry of the PFD with respect to a minimized misfit between the predicted thermoelastic deformation and observed deformation. Prediction errors from an FEM, which includes an optimized PFD geometry and the initial excess PFD temperature estimated from the least-squares analysis, are sub-millimeter (RMSE = 0.3 mm). The average thickness (9.3 m), maximum thickness (126 m), and volume (2.1 × 107m3) of the PFD, estimated using the adaptive mesh algorithm, are about twice as large as the respective estimations for the a priori PFD geometry. Sensitivity analyses suggest unrealistic PFD thickness distributions are required for initial excess PFD temperatures outside of the range 500–800 °C.

A Regional Analysis of Non-Methane Hydrocarbons And Meteorology of The Rural Southeast United States

DTIC Science & Technology

1996-01-01

Zt is an ARIMA time series. This is a typical regression model , except that it allows for autocorrelation in the error term Z. In this work, an ARMA...data=folder; var residual; run; II Statistical output of 1992 regression model on 1993 ozone data ARIMA Procedure Maximum Likelihood Estimation Approx...at each of the sites, and to show the effect of synoptic meteorology on high ozone by examining NOAA daily weather maps and climatic data

Extracting Maximum Total Water Levels from Video "Brightest" Images

NASA Astrophysics Data System (ADS)

Brown, J. A.; Holman, R. A.; Stockdon, H. F.; Plant, N. G.; Long, J.; Brodie, K.

2016-02-01

An important parameter for predicting storm-induced coastal change is the maximum total water level (TWL). Most studies estimate the TWL as the sum of slowly varying water levels, including tides and storm surge, and the extreme runup parameter R2%, which includes wave setup and swash motions over minutes to seconds. Typically, R2% is measured using video remote sensing data, where cross-shore timestacks of pixel intensity are digitized to extract the horizontal runup timeseries. However, this technique must be repeated at multiple alongshore locations to resolve alongshore variability, and can be tedious and time consuming. We seek an efficient, video-based approach that yields a synoptic estimate of TWL that accounts for alongshore variability and can be applied during storms. In this work, the use of a video product termed the "brightest" image is tested; this represents the highest intensity of each pixel captured during a 10-minute collection period. Image filtering and edge detection techniques are applied to automatically determine the shoreward edge of the brightest region (i.e., the swash zone) at each alongshore pixel. The edge represents the horizontal position of the maximum TWL along the beach during the collection period, and is converted to vertical elevations using measured beach topography. This technique is evaluated using video and topographic data collected every half-hour at Duck, NC, during differing hydrodynamic conditions. Relationships between the maximum TWL estimates from the brightest images and various runup statistics computed using concurrent runup timestacks are examined, and errors associated with mapping the horizontal results to elevations are discussed. This technique is invaluable, as it can be used to routinely estimate maximum TWLs along a coastline from a single brightest image product, and provides a means for examining alongshore variability of TWLs at high alongshore resolution. These advantages will be useful in validating numerical hydrodynamic models and improving coastal change predictions.

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Efficient logistic regression designs under an imperfect population identifier.

PubMed

Albert, Paul S; Liu, Aiyi; Nansel, Tonja

2014-03-01

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial. © 2013, The International Biometric Society.

Field evaluation of distance-estimation error during wetland-dependent bird surveys

USGS Publications Warehouse

Nadeau, Christopher P.; Conway, Courtney J.

2012-01-01

Context: The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims: We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods: We used two approaches to estimate the error associated with five surveyor's distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor's ability to estimate distance. Key results: We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (x̄error = -9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (x̄error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions: Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications: The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

NASA Astrophysics Data System (ADS)

King, Tyler V.; Neilson, Bethany T.; Rasmussen, Mitchell T.

2018-02-01

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating volumetric river discharge in low-order (<50 m wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substantial portion of the river bed. Topography of the exposed river bed was photogrammetrically extracted from high-resolution aerial imagery while the geometry of the remaining inundated portion of the channel was approximated based on adjacent bank topography and maximum depth assumptions. Full channel bathymetry was used to create hydraulic models that encompassed virtual gauging stations. Discharge for each aerial survey was estimated with the hydraulic model by matching modeled and remotely sensed wetted widths. Based on these results, synthetic width-discharge rating curves were produced for each virtual gauging station. In situ observations were used to determine the accuracy of wetted widths extracted from imagery (mean error 0.36 m), extracted bathymetry (mean vertical RMSE 0.23 m), and discharge (mean percent error 7% with a standard deviation of 6%). Sensitivity analyses were conducted to determine the influence of inundated channel bathymetry and roughness parameters on estimated discharge. Comparison of synthetic rating curves produced through sensitivity analyses show that reasonable ranges of parameter values result in mean percent errors in predicted discharges of 12%-27%.

A Fortran 77 computer code for damped least-squares inversion of Slingram electromagnetic anomalies over thin tabular conductors

NASA Astrophysics Data System (ADS)

Dondurur, Derman; Sarı, Coşkun

2004-07-01

A FORTRAN 77 computer code is presented that permits the inversion of Slingram electromagnetic anomalies to an optimal conductor model. Damped least-squares inversion algorithm is used to estimate the anomalous body parameters, e.g. depth, dip and surface projection point of the target. Iteration progress is controlled by maximum relative error value and iteration continued until a tolerance value was satisfied, while the modification of Marquardt's parameter is controlled by sum of the squared errors value. In order to form the Jacobian matrix, the partial derivatives of theoretical anomaly expression with respect to the parameters being optimised are calculated by numerical differentiation by using first-order forward finite differences. A theoretical and two field anomalies are inserted to test the accuracy and applicability of the present inversion program. Inversion of the field data indicated that depth and the surface projection point parameters of the conductor are estimated correctly, however, considerable discrepancies appeared on the estimated dip angles. It is therefore concluded that the most important factor resulting in the misfit between observed and calculated data is due to the fact that the theory used for computing Slingram anomalies is valid for only thin conductors and this assumption might have caused incorrect dip estimates in the case of wide conductors.

A comparison of three approaches to non-stationary flood frequency analysis

NASA Astrophysics Data System (ADS)

Debele, S. E.; Strupczewski, W. G.; Bogdanowicz, E.

2017-08-01

Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with non-stationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled "Around and about an application of the GAMLSS package in non-stationary flood frequency analysis".

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

PubMed

Davidov, Ori; Rosen, Sophia

2011-04-01

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

An internal reference model-based PRF temperature mapping method with Cramer-Rao lower bound noise performance analysis.

PubMed

Li, Cheng; Pan, Xinyi; Ying, Kui; Zhang, Qiang; An, Jing; Weng, Dehe; Qin, Wen; Li, Kuncheng

2009-11-01

The conventional phase difference method for MR thermometry suffers from disturbances caused by the presence of lipid protons, motion-induced error, and field drift. A signal model is presented with multi-echo gradient echo (GRE) sequence using a fat signal as an internal reference to overcome these problems. The internal reference signal model is fit to the water and fat signals by the extended Prony algorithm and the Levenberg-Marquardt algorithm to estimate the chemical shifts between water and fat which contain temperature information. A noise analysis of the signal model was conducted using the Cramer-Rao lower bound to evaluate the noise performance of various algorithms, the effects of imaging parameters, and the influence of the water:fat signal ratio in a sample on the temperature estimate. Comparison of the calculated temperature map and thermocouple temperature measurements shows that the maximum temperature estimation error is 0.614 degrees C, with a standard deviation of 0.06 degrees C, confirming the feasibility of this model-based temperature mapping method. The influence of sample water:fat signal ratio on the accuracy of the temperature estimate is evaluated in a water-fat mixed phantom experiment with an optimal ratio of approximately 0.66:1. (c) 2009 Wiley-Liss, Inc.

Estimating Population Cause-Specific Mortality Fractions from in-Hospital Mortality: Validation of a New Method

PubMed Central

Murray, Christopher J. L; Lopez, Alan D; Barofsky, Jeremy T; Bryson-Cahn, Chloe; Lozano, Rafael

2007-01-01

Background Cause-of-death data for many developing countries are not available. Information on deaths in hospital by cause is available in many low- and middle-income countries but is not a representative sample of deaths in the population. We propose a method to estimate population cause-specific mortality fractions (CSMFs) using data already collected in many middle-income and some low-income developing nations, yet rarely used: in-hospital death records. Methods and Findings For a given cause of death, a community's hospital deaths are equal to total community deaths multiplied by the proportion of deaths occurring in hospital. If we can estimate the proportion dying in hospital, we can estimate the proportion dying in the population using deaths in hospital. We propose to estimate the proportion of deaths for an age, sex, and cause group that die in hospital from the subset of the population where vital registration systems function or from another population. We evaluated our method using nearly complete vital registration (VR) data from Mexico 1998–2005, which records whether a death occurred in a hospital. In this validation test, we used 45 disease categories. We validated our method in two ways: nationally and between communities. First, we investigated how the method's accuracy changes as we decrease the amount of Mexican VR used to estimate the proportion of each age, sex, and cause group dying in hospital. Decreasing VR data used for this first step from 100% to 9% produces only a 12% maximum relative error between estimated and true CSMFs. Even if Mexico collected full VR information only in its capital city with 9% of its population, our estimation method would produce an average relative error in CSMFs across the 45 causes of just over 10%. Second, we used VR data for the capital zone (Distrito Federal and Estado de Mexico) and estimated CSMFs for the three lowest-development states. Our estimation method gave an average relative error of 20%, 23%, and 31% for Guerrero, Chiapas, and Oaxaca, respectively. Conclusions Where accurate International Classification of Diseases (ICD)-coded cause-of-death data are available for deaths in hospital and for VR covering a subset of the population, we demonstrated that population CSMFs can be estimated with low average error. In addition, we showed in the case of Mexico that this method can substantially reduce error from biased hospital data, even when applied to areas with widely different levels of development. For countries with ICD-coded deaths in hospital, this method potentially allows the use of existing data to inform health policy. PMID:18031195

Revisiting the Estimation of Dinosaur Growth Rates

PubMed Central

Myhrvold, Nathan P.

2013-01-01

Previous growth-rate studies covering 14 dinosaur taxa, as represented by 31 data sets, are critically examined and reanalyzed by using improved statistical techniques. The examination reveals that some previously reported results cannot be replicated by using the methods originally reported; results from new methods are in many cases different, in both the quantitative rates and the qualitative nature of the growth, from results in the prior literature. Asymptotic growth curves, which have been hypothesized to be ubiquitous, are shown to provide best fits for only four of the 14 taxa. Possible reasons for non-asymptotic growth patterns are discussed; they include systematic errors in the age-estimation process and, more likely, a bias toward younger ages among the specimens analyzed. Analysis of the data sets finds that only three taxa include specimens that could be considered skeletally mature (i.e., having attained 90% of maximum body size predicted by asymptotic curve fits), and eleven taxa are quite immature, with the largest specimen having attained less than 62% of predicted asymptotic size. The three taxa that include skeletally mature specimens are included in the four taxa that are best fit by asymptotic curves. The totality of results presented here suggests that previous estimates of both maximum dinosaur growth rates and maximum dinosaur sizes have little statistical support. Suggestions for future research are presented. PMID:24358133

Cost-effectiveness of the stream-gaging program in Kentucky

USGS Publications Warehouse

Ruhl, K.J.

1989-01-01

This report documents the results of a study of the cost-effectiveness of the stream-gaging program in Kentucky. The total surface-water program includes 97 daily-discharge stations , 12 stage-only stations, and 35 crest-stage stations and is operated on a budget of $950,700. One station used for research lacks adequate source of funding and should be discontinued when the research ends. Most stations in the network are multiple-use with 65 stations operated for the purpose of defining hydrologic systems, 48 for project operation, 47 for definition of regional hydrology, and 43 for hydrologic forecasting purposes. Eighteen stations support water quality monitoring activities, one station is used for planning and design, and one station is used for research. The average standard error of estimation of streamflow records was determined only for stations in the Louisville Subdistrict. Under current operating policy, with a budget of $223,500, the average standard error of estimation is 28.5%. Altering the travel routes and measurement frequency to reduce the amount of lost stage record would allow a slight decrease in standard error to 26.9%. The results indicate that the collection of streamflow records in the Louisville Subdistrict is cost effective in its present mode of operation. In the Louisville Subdistrict, a minimum budget of $214,200 is required to operate the current network at an average standard error of 32.7%. A budget less than this does not permit proper service and maintenance of the gages and recorders. The maximum budget analyzed was $268,200, which would result in an average standard error of 16.9% indicating that if the budget was increased by 20%, the percent standard error would be reduced 40 %. (USGS)

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

Using multiple linear regression model to estimate thunderstorm activity

NASA Astrophysics Data System (ADS)

Suparta, W.; Putro, W. S.

2017-03-01

This paper is aimed to develop a numerical model with the use of a nonlinear model to estimate the thunderstorm activity. Meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), cloud (C), Precipitable Water Vapor (PWV), and precipitation on a daily basis were used in the proposed method. The model was constructed with six configurations of input and one target output. The output tested in this work is the thunderstorm event when one-year data is used. Results showed that the model works well in estimating thunderstorm activities with the maximum epoch reaching 1000 iterations and the percent error was found below 50%. The model also found that the thunderstorm activities in May and October are detected higher than the other months due to the inter-monsoon season.

Bias in the Wagner-Nelson estimate of the fraction of drug absorbed.

PubMed

Wang, Yibin; Nedelman, Jerry

2002-04-01

To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CV% for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r = 0.5, 3, or 6; k = 0.1 or 0.2; CV, = 0.2 or 0.4; and CV, =0.2 or 0.4; for t = 0 to 30 or 60. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CV,. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early absorption phase. Measurement error of concentration does not introduce bias. The joint effect of estimation error of k and truncation error in AUC can result in a non-monotonic fraction-of-drug-absorbed-vs-time profile. However, only estimation error of k can lead to the Wagner-Nelson estimate of fraction of drug absorbed greater than unity.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Angular motion estimation using dynamic models in a gyro-free inertial measurement unit.

PubMed

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters.

Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit

PubMed Central

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters. PMID:22778586

Effects of error covariance structure on estimation of model averaging weights and predictive performance

USGS Publications Warehouse

Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

2013-01-01

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

An Upper Bound on Orbital Debris Collision Probability When Only One Object has Position Uncertainty Information

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2015-01-01

Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do no good because the analyst defaults to no knowledge of the combined, relative position error covariance matrix. It is reasonable to think, given an assumption of no covariance information, an analyst might still attempt to determine the error covariance matrix that results in an upper bound on the P (sub c). Without some guidance on limits to the shape, size and orientation of the unknown covariance matrix, the limiting case is a degenerate ellipse lying along the relative miss vector in the collision plane. Unless the miss position is exceptionally large or the at-risk object is exceptionally small, this method results in a maximum P (sub c) too large to be of practical use. For example, assuming that the miss distance is equal to the current ISS alert volume along-track (+ or -) distance of 25 kilometers and that the at-risk area has a 70 meter radius. The maximum (degenerate ellipse) P (sub c) is about 0.00136. At 40 kilometers, the maximum P (sub c) would be 0.00085 which is still almost an order of magnitude larger than the ISS maneuver threshold of 0.0001. In fact, a miss distance of almost 340 kilometers is necessary to reduce the maximum P (sub c) associated with this degenerate ellipse to the ISS maneuver threshold value. Such a result is frequently of no practical value to the analyst. Some improvement may be made with respect to this problem by realizing that while the position error covariance matrix of one of the objects (usually the debris object) may not be known the position error covariance matrix of the other object (usually the asset) is almost always available. Making use of the position error covariance information for the one object provides an improvement in finding a maximum P (sub c) which, in some cases, may offer real utility. The equations to be used are presented and their use discussed.

Development and evaluation of a Kalman-filter algorithm for terminal area navigation using sensors of moderate accuracy

NASA Technical Reports Server (NTRS)

Kanning, G.; Cicolani, L. S.; Schmidt, S. F.

1983-01-01

Translational state estimation in terminal area operations, using a set of commonly available position, air data, and acceleration sensors, is described. Kalman filtering is applied to obtain maximum estimation accuracy from the sensors but feasibility in real-time computations requires a variety of approximations and devices aimed at minimizing the required computation time with only negligible loss of accuracy. Accuracy behavior throughout the terminal area, its relation to sensor accuracy, its effect on trajectory tracking errors and control activity in an automatic flight control system, and its adequacy in terms of existing criteria for various terminal area operations are examined. The principal investigative tool is a simulation of the system.

Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals.

PubMed

Jeyhani, Vala; Mahdiani, Shadi; Peltokangas, Mikko; Vehkaoja, Antti

2015-01-01

Heart rate variability (HRV) has become a useful tool in analysis of cardiovascular system in both research and clinical fields. HRV has been also used in other applications such as stress level estimation in wearable devices. HRV is normally obtained from ECG as the time interval of two successive R waves. Recently PPG has been proposed as an alternative for ECG in HRV analysis to overcome some difficulties in measurement of ECG. In addition, PPG-HRV is also used in some commercial devices such as modern optical wrist-worn heart rate monitors. However, some researches have shown that PPG is not a surrogate for heart rate variability analysis. In this work, HRV analysis was applied on beat-to-beat intervals obtained from ECG and PPG in 19 healthy male subjects. Some important HRV parameters were calculated from PPG-HRV and ECG-HRV. Maximum of PPG and its second derivative were considered as two methods for obtaining the beat-to-beat signals from PPG and the results were compared with those achieved from ECG-HRV. Our results show that the smallest error happens in SDNN and SD2 with relative error of 2.46% and 2%, respectively. The most affected parameter is pNN50 with relative error of 29.89%. In addition, in our trial, using the maximum of PPG gave better results than its second derivative.

Bayesian statistics applied to the location of the source of explosions at Stromboli Volcano, Italy

USGS Publications Warehouse

Saccorotti, G.; Chouet, B.; Martini, M.; Scarpa, R.

1998-01-01

We present a method for determining the location and spatial extent of the source of explosions at Stromboli Volcano, Italy, based on a Bayesian inversion of the slowness vector derived from frequency-slowness analyses of array data. The method searches for source locations that minimize the error between the expected and observed slowness vectors. For a given set of model parameters, the conditional probability density function of slowness vectors is approximated by a Gaussian distribution of expected errors. The method is tested with synthetics using a five-layer velocity model derived for the north flank of Stromboli and a smoothed velocity model derived from a power-law approximation of the layered structure. Application to data from Stromboli allows for a detailed examination of uncertainties in source location due to experimental errors and incomplete knowledge of the Earth model. Although the solutions are not constrained in the radial direction, excellent resolution is achieved in both transverse and depth directions. Under the assumption that the horizontal extent of the source does not exceed the crater dimension, the 90% confidence region in the estimate of the explosive source location corresponds to a small volume extending from a depth of about 100 m to a maximum depth of about 300 m beneath the active vents, with a maximum likelihood source region located in the 120- to 180-m-depth interval.

Comparison between geodetic and oceanographic approaches to estimate mean dynamic topography for vertical datum unification: evaluation at Australian tide gauges

NASA Astrophysics Data System (ADS)

Filmer, M. S.; Hughes, C. W.; Woodworth, P. L.; Featherstone, W. E.; Bingham, R. J.

2018-04-01

The direct method of vertical datum unification requires estimates of the ocean's mean dynamic topography (MDT) at tide gauges, which can be sourced from either geodetic or oceanographic approaches. To assess the suitability of different types of MDT for this purpose, we evaluate 13 physics-based numerical ocean models and six MDTs computed from observed geodetic and/or ocean data at 32 tide gauges around the Australian coast. We focus on the viability of numerical ocean models for vertical datum unification, classifying the 13 ocean models used as either independent (do not contain assimilated geodetic data) or non-independent (do contain assimilated geodetic data). We find that the independent and non-independent ocean models deliver similar results. Maximum differences among ocean models and geodetic MDTs reach >150 mm at several Australian tide gauges and are considered anomalous at the 99% confidence level. These differences appear to be of geodetic origin, but without additional independent information, or formal error estimates for each model, some of these errors remain inseparable. Our results imply that some ocean models have standard deviations of differences with other MDTs (using geodetic and/or ocean observations) at Australian tide gauges, and with levelling between some Australian tide gauges, of ˜ ± 50 mm . This indicates that they should be considered as an alternative to geodetic MDTs for the direct unification of vertical datums. They can also be used as diagnostics for errors in geodetic MDT in coastal zones, but the inseparability problem remains, where the error cannot be discriminated between the geoid model or altimeter-derived mean sea surface.

Statistical modelling of thermal annealing of fission tracks in apatite

NASA Astrophysics Data System (ADS)

Laslett, G. M.; Galbraith, R. F.

1996-12-01

We develop an improved methodology for modelling the relationship between mean track length, temperature, and time in fission track annealing experiments. We consider "fanning Arrhenius" models, in which contours of constant mean length on an Arrhenius plot are straight lines meeting at a common point. Features of our approach are explicit use of subject matter knowledge, treating mean length as the response variable, modelling of the mean-variance relationship with two components of variance, improved modelling of the control sample, and using information from experiments in which no tracks are seen. This approach overcomes several weaknesses in previous models and provides a robust six parameter model that is widely applicable. Estimation is via direct maximum likelihood which can be implemented using a standard numerical optimisation package. Because the model is highly nonlinear, some reparameterisations are needed to achieve stable estimation and calculation of precisions. Experience suggests that precisions are more convincingly estimated from profile log-likelihood functions than from the information matrix. We apply our method to the B-5 and Sr fluorapatite data of Crowley et al. (1991) and obtain well-fitting models in both cases. For the B-5 fluorapatite, our model exhibits less fanning than that of Crowley et al. (1991), although fitted mean values above 12 μm are fairly similar. However, predictions can be different, particularly for heavy annealing at geological time scales, where our model is less retentive. In addition, the refined error structure of our model results in tighter prediction errors, and has components of error that are easier to verify or modify. For the Sr fluorapatite, our fitted model for mean lengths does not differ greatly from that of Crowley et al. (1991), but our error structure is quite different.

Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

PubMed

Gustafsson, Mats G; Wallman, Mikael; Wickenberg Bolin, Ulrika; Göransson, Hanna; Fryknäs, M; Andersson, Claes R; Isaksson, Anders

2010-06-01

Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice. It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples. Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets. An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier. Copyright 2010 Elsevier B.V. All rights reserved.

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

NASA Technical Reports Server (NTRS)

Adler, Robert; Gu, Guojun; Huffman, George

2012-01-01

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Analysis of the load selection on the error of source characteristics identification for an engine exhaust system

NASA Astrophysics Data System (ADS)

Zheng, Sifa; Liu, Haitao; Dan, Jiabi; Lian, Xiaomin

2015-05-01

Linear time-invariant assumption for the determination of acoustic source characteristics, the source strength and the source impedance in the frequency domain has been proved reasonable in the design of an exhaust system. Different methods have been proposed to its identification and the multi-load method is widely used for its convenience by varying the load number and impedance. Theoretical error analysis has rarely been referred to and previous results have shown an overdetermined set of open pipes can reduce the identification error. This paper contributes a theoretical error analysis for the load selection. The relationships between the error in the identification of source characteristics and the load selection were analysed. A general linear time-invariant model was built based on the four-load method. To analyse the error of the source impedance, an error estimation function was proposed. The dispersion of the source pressure was obtained by an inverse calculation as an indicator to detect the accuracy of the results. It was found that for a certain load length, the load resistance at the frequency points of one-quarter wavelength of odd multiples results in peaks and in the maximum error for source impedance identification. Therefore, the load impedance of frequency range within the one-quarter wavelength of odd multiples should not be used for source impedance identification. If the selected loads have more similar resistance values (i.e., the same order of magnitude), the identification error of the source impedance could be effectively reduced.

Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia

PubMed Central

Huang, Chiung-Yu; Qin, Jing

2013-01-01

The Canadian Study of Health and Aging (CSHA) employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. The prevalent cohort sampling scheme favors individuals who survive longer. Thus, the observed survival times are subject to length bias. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. This article considers semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Moreover, the asymptotic properties of the semiparametric maximum likelihood estimator are not well-studied. Motivated by the composite likelihood method (Besag 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software. When applied to the CSHA data, the proposed method estimates a significant difference in survival between the vascular dementia group and the possible Alzheimer’s disease group, while the partial likelihood method for left-truncated and right-censored data yields a greater standard error and a 95% confidence interval covering 0, thus highlighting the practical value of employing a more efficient methodology. To check the assumption of stable disease for the CSHA data, we also present new graphical and numerical tests in the article. The R code used to obtain the maximum composite partial likelihood estimator for the CSHA data is available in the online Supplementary Material, posted on the journal web site. PMID:24000265

Voluntary EMG-to-force estimation with a multi-scale physiological muscle model

PubMed Central

2013-01-01

Background EMG-to-force estimation based on muscle models, for voluntary contraction has many applications in human motion analysis. The so-called Hill model is recognized as a standard model for this practical use. However, it is a phenomenological model whereby muscle activation, force-length and force-velocity properties are considered independently. Perreault reported Hill modeling errors were large for different firing frequencies, level of activation and speed of contraction. It may be due to the lack of coupling between activation and force-velocity properties. In this paper, we discuss EMG-force estimation with a multi-scale physiology based model, which has a link to underlying crossbridge dynamics. Differently from the Hill model, the proposed method provides dual dynamics of recruitment and calcium activation. Methods The ankle torque was measured for the plantar flexion along with EMG measurements of the medial gastrocnemius (GAS) and soleus (SOL). In addition to Hill representation of the passive elements, three models of the contractile parts have been compared. Using common EMG signals during isometric contraction in four able-bodied subjects, torque was estimated by the linear Hill model, the nonlinear Hill model and the multi-scale physiological model that refers to Huxley theory. The comparison was made in normalized scale versus the case in maximum voluntary contraction. Results The estimation results obtained with the multi-scale model showed the best performances both in fast-short and slow-long term contraction in randomized tests for all the four subjects. The RMS errors were improved with the nonlinear Hill model compared to linear Hill, however it showed limitations to account for the different speed of contractions. Average error was 16.9% with the linear Hill model, 9.3% with the modified Hill model. In contrast, the error in the multi-scale model was 6.1% while maintaining a uniform estimation performance in both fast and slow contractions schemes. Conclusions We introduced a novel approach that allows EMG-force estimation based on a multi-scale physiology model integrating Hill approach for the passive elements and microscopic cross-bridge representations for the contractile element. The experimental evaluation highlights estimation improvements especially a larger range of contraction conditions with integration of the neural activation frequency property and force-velocity relationship through cross-bridge dynamics consideration. PMID:24007560

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Design and analysis of three-arm trials with negative binomially distributed endpoints.

PubMed

Mütze, Tobias; Munk, Axel; Friede, Tim

2016-02-20

A three-arm clinical trial design with an experimental treatment, an active control, and a placebo control, commonly referred to as the gold standard design, enables testing of non-inferiority or superiority of the experimental treatment compared with the active control. In this paper, we propose methods for designing and analyzing three-arm trials with negative binomially distributed endpoints. In particular, we develop a Wald-type test with a restricted maximum-likelihood variance estimator for testing non-inferiority or superiority. For this test, sample size and power formulas as well as optimal sample size allocations will be derived. The performance of the proposed test will be assessed in an extensive simulation study with regard to type I error rate, power, sample size, and sample size allocation. For the purpose of comparison, Wald-type statistics with a sample variance estimator and an unrestricted maximum-likelihood estimator are included in the simulation study. We found that the proposed Wald-type test with a restricted variance estimator performed well across the considered scenarios and is therefore recommended for application in clinical trials. The methods proposed are motivated and illustrated by a recent clinical trial in multiple sclerosis. The R package ThreeArmedTrials, which implements the methods discussed in this paper, is available on CRAN. Copyright © 2015 John Wiley & Sons, Ltd.

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

Nonconservative force model parameter estimation strategy for TOPEX/Poseidon precision orbit determination

NASA Technical Reports Server (NTRS)

Luthcke, S. B.; Marshall, J. A.

1992-01-01

The TOPEX/Poseidon spacecraft was launched on August 10, 1992 to study the Earth's oceans. To achieve maximum benefit from the altimetric data it is to collect, mission requirements dictate that TOPEX/Poseidon's orbit must be computed at an unprecedented level of accuracy. To reach our pre-launch radial orbit accuracy goals, the mismodeling of the radiative nonconservative forces of solar radiation, Earth albedo an infrared re-radiation, and spacecraft thermal imbalances cannot produce in combination more than a 6 cm rms error over a 10 day period. Similarly, the 10-day drag modeling error cannot exceed 3 cm rms. In order to satisfy these requirements, a 'box-wing' representation of the satellite has been developed in which, the satellite is modelled as the combination of flat plates arranged in the shape of a box and a connected solar array. The radiative/thermal nonconservative forces acting on each of the eight surfaces are computed independently, yielding vector accelerations which are summed to compute the total aggregate effect on the satellite center-of-mass. Select parameters associated with the flat plates are adjusted to obtain a better representation of the satellite acceleration history. This study analyzes the estimation of these parameters from simulated TOPEX/Poseidon laser data in the presence of both nonconservative and gravity model errors. A 'best choice' of estimated parameters is derived and the ability to meet mission requirements with the 'box-wing' model evaluated.

Common mode error in Antarctic GPS coordinate time series on its effect on bedrock-uplift estimates

NASA Astrophysics Data System (ADS)

Liu, Bin; King, Matt; Dai, Wujiao

2018-05-01

Spatially-correlated common mode error always exists in regional, or-larger, GPS networks. We applied independent component analysis (ICA) to GPS vertical coordinate time series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are common mode error (CME). An average reduction of ˜40% about the RMS values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time series present interesting correlations with modeled atmospheric and non-tidal ocean loading displacements. A white noise (WN) plus power law noise (PL) model was adopted in the GPS velocity estimation using maximum likelihood estimation (MLE) analysis, with ˜55% reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ˜0.9 mm/yr of the WRMS values, possibly allowing for more confident separation of various GIA model predictions.

Cost-effectiveness of the stream-gaging program in Missouri

USGS Publications Warehouse

Waite, L.A.

1987-01-01

This report documents the results of an evaluation of the cost effectiveness of the 1986 stream-gaging program in Missouri. Alternative methods of developing streamflow information and cost-effective resource allocation were used to evaluate the Missouri program. Alternative methods were considered statewide, but the cost effective resource allocation study was restricted to the area covered by the Rolla field headquarters. The average standard error of estimate for records of instantaneous discharge was 17 percent; assuming the 1986 budget and operating schedule, it was shown that this overall degree of accuracy could be improved to 16 percent by altering the 1986 schedule of station visitations. A minimum budget of $203,870, with a corresponding average standard error of estimate 17 percent, is required to operate the 1986 program for the Rolla field headquarters; a budget of less than this would not permit proper service and maintenance of the stations or adequate definition of stage-discharge relations. The maximum budget analyzed was $418,870, which resulted in an average standard error of estimate of 14 percent. Improved instrumentation can have a positive effect on streamflow uncertainties by decreasing lost records. An earlier study of data uses found that data uses were sufficient to justify continued operation of all stations. One of the stations investigated, Current River at Doniphan (07068000) was suitable for the application of alternative methods for simulating discharge records. However, the station was continued because of data use requirements. (Author 's abstract)

Calculating sediment discharge from a highway construction site in central Pennsylvania

USGS Publications Warehouse

Reed, L.A.; Ward, J.R.; Wetzel, K.L.

1985-01-01

The Pennsylvania Department of Transportation, the Federal Highway Administration, and the U.S. Geological Survey have cooperated in a study to evaluate two methods of predicting sediment yields during highway construction. Sediment yields were calculated using the Universal Soil Loss and the Younkin Sediment Prediction Equations. Results were compared to the actual measured values, and standard errors and coefficients of correlation were calculated. Sediment discharge from the construction area was determined for storms that occurred during construction of Interstate 81 in a 0.38-square mile basin near Harrisburg, Pennsylvania. Precipitation data tabulated included total rainfall, maximum 30-minute rainfall, kinetic energy, and the erosive index of the precipitation. Highway construction data tabulated included the area disturbed by clearing and grubbing, the area in cuts and fills, the average depths of cuts and fills, the area seeded and mulched, and the area paved. Using the Universal Soil Loss Equation, sediment discharge from the construction area was calculated for storms. The standard error of estimate was 0.40 (about 105 percent), and the coefficient of correlation was 0.79. Sediment discharge from the construction area was also calculated using the Younkin Equation. The standard error of estimate of 0.42 (about 110 percent), and the coefficient of correlation of 0.77 are comparable to those from the Universal Soil Loss Equation.

Skeletal Correlates for Body Mass Estimation in Modern and Fossil Flying Birds

PubMed Central

Field, Daniel J.; Lynner, Colton; Brown, Christian; Darroch, Simon A. F.

2013-01-01

Scaling relationships between skeletal dimensions and body mass in extant birds are often used to estimate body mass in fossil crown-group birds, as well as in stem-group avialans. However, useful statistical measurements for constraining the precision and accuracy of fossil mass estimates are rarely provided, which prevents the quantification of robust upper and lower bound body mass estimates for fossils. Here, we generate thirteen body mass correlations and associated measures of statistical robustness using a sample of 863 extant flying birds. By providing robust body mass regressions with upper- and lower-bound prediction intervals for individual skeletal elements, we address the longstanding problem of body mass estimation for highly fragmentary fossil birds. We demonstrate that the most precise proxy for estimating body mass in the overall dataset, measured both as coefficient determination of ordinary least squares regression and percent prediction error, is the maximum diameter of the coracoid’s humeral articulation facet (the glenoid). We further demonstrate that this result is consistent among the majority of investigated avian orders (10 out of 18). As a result, we suggest that, in the majority of cases, this proxy may provide the most accurate estimates of body mass for volant fossil birds. Additionally, by presenting statistical measurements of body mass prediction error for thirteen different body mass regressions, this study provides a much-needed quantitative framework for the accurate estimation of body mass and associated ecological correlates in fossil birds. The application of these regressions will enhance the precision and robustness of many mass-based inferences in future paleornithological studies. PMID:24312392

New estimates of the CMB angular power spectra from the WMAP 5 year low-resolution data

NASA Astrophysics Data System (ADS)

Gruppuso, A.; de Rosa, A.; Cabella, P.; Paci, F.; Finelli, F.; Natoli, P.; de Gasperis, G.; Mandolesi, N.

2009-11-01

A quadratic maximum likelihood (QML) estimator is applied to the Wilkinson Microwave Anisotropy Probe (WMAP) 5 year low-resolution maps to compute the cosmic microwave background angular power spectra (APS) at large scales for both temperature and polarization. Estimates and error bars for the six APS are provided up to l = 32 and compared, when possible, to those obtained by the WMAP team, without finding any inconsistency. The conditional likelihood slices are also computed for the Cl of all the six power spectra from l = 2 to 10 through a pixel-based likelihood code. Both the codes treat the covariance for (T, Q, U) in a single matrix without employing any approximation. The inputs of both the codes (foreground-reduced maps, related covariances and masks) are provided by the WMAP team. The peaks of the likelihood slices are always consistent with the QML estimates within the error bars; however, an excellent agreement occurs when the QML estimates are used as a fiducial power spectrum instead of the best-fitting theoretical power spectrum. By the full computation of the conditional likelihood on the estimated spectra, the value of the temperature quadrupole CTTl=2 is found to be less than 2σ away from the WMAP 5 year Λ cold dark matter best-fitting value. The BB spectrum is found to be well consistent with zero, and upper limits on the B modes are provided. The parity odd signals TB and EB are found to be consistent with zero.

Predicting tropical cyclone intensity using satellite measured equivalent blackbody temperatures of cloud tops. [regression analysis

NASA Technical Reports Server (NTRS)

Gentry, R. C.; Rodgers, E.; Steranka, J.; Shenk, W. E.

1978-01-01

A regression technique was developed to forecast 24 hour changes of the maximum winds for weak (maximum winds less than or equal to 65 Kt) and strong (maximum winds greater than 65 Kt) tropical cyclones by utilizing satellite measured equivalent blackbody temperatures around the storm alone and together with the changes in maximum winds during the preceding 24 hours and the current maximum winds. Independent testing of these regression equations shows that the mean errors made by the equations are lower than the errors in forecasts made by the peristence techniques.

On the possibility of non-invasive multilayer temperature estimation using soft-computing methods.

PubMed

Teixeira, C A; Pereira, W C A; Ruano, A E; Ruano, M Graça

2010-01-01

This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e.g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar-agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities (I(SATA)): 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2.0W/cm(2). Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 degrees C. The best-fitted estimator presents a MAE of only 0.4 degrees C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 degrees C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time. A non-invasive temperature estimation model, based on soft-computing technique, was proposed for a three-layered phantom. The best-achieved estimator models presented an appropriate error performance regardless of the spatial point considered (inside or at the interface of the layers) and of the intensity applied. Other methodologies published so far, estimate temperature only in homogeneous media. The main drawback of the proposed methodology is the necessity of a-priory knowledge of the temperature behavior. Data used for training and optimisation should be representative, i.e., they should cover all possible physical situations of the estimation environment.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

Real-time stop sign detection and distance estimation using a single camera

NASA Astrophysics Data System (ADS)

Wang, Wenpeng; Su, Yuxuan; Cheng, Ming

2018-04-01

In modern world, the drastic development of driver assistance system has made driving a lot easier than before. In order to increase the safety onboard, a method was proposed to detect STOP sign and estimate distance using a single camera. In STOP sign detection, LBP-cascade classifier was applied to identify the sign in the image, and the principle of pinhole imaging was based for distance estimation. Road test was conducted using a detection system built with a CMOS camera and software developed by Python language with OpenCV library. Results shows that that the proposed system reach a detection accuracy of maximum of 97.6% at 10m, a minimum of 95.00% at 20m, and 5% max error in distance estimation. The results indicate that the system is effective and has the potential to be used in both autonomous driving and advanced driver assistance driving systems.

Fast and accurate read-out of interferometric optical fiber sensors

NASA Astrophysics Data System (ADS)

Bartholsen, Ingebrigt; Hjelme, Dag R.

2016-03-01

We present results from an evaluation of phase and frequency estimation algorithms for read-out instrumentation of interferometric sensors. Tests on interrogating a micro Fabry-Perot sensor made of semi-spherical stimuli-responsive hydrogel immobilized on a single mode fiber end face, shows that an iterative quadrature demodulation technique (IQDT) implemented on a 32-bit microcontroller unit can achieve an absolute length accuracy of ±50 nm and length change accuracy of ±3 nm using an 80 nm SLED source and a grating spectrometer for interrogation. The mean absolute error for the frequency estimator is a factor 3 larger than the theoretical lower bound for a maximum likelihood estimator. The corresponding factor for the phase estimator is 1.3. The computation time for the IQDT algorithm is reduced by a factor 1000 compared to the full QDT for the same accuracy requirement.

A Bayesian approach to tracking patients having changing pharmacokinetic parameters

NASA Technical Reports Server (NTRS)

Bayard, David S.; Jelliffe, Roger W.

2004-01-01

This paper considers the updating of Bayesian posterior densities for pharmacokinetic models associated with patients having changing parameter values. For estimation purposes it is proposed to use the Interacting Multiple Model (IMM) estimation algorithm, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and Maximum A-Posteriori (MAP) Bayesian estimation methods, which are presently used for posterior updating when pharmacokinetic parameters do not change. Both the MM and MAP Bayesian estimation methods are used in their sequential forms, to facilitate tracking of changing parameters. Results indicate that the IMM algorithm is well suited for tracking time-varying pharmacokinetic parameters in acutely ill and unstable patients, incurring only about half of the integrated error compared to the sequential MM and MAP methods on the same example.

A joint source-channel distortion model for JPEG compressed images.

PubMed

Sabir, Muhammad F; Sheikh, Hamid Rahim; Heath, Robert W; Bovik, Alan C

2006-06-01

The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.

Identification and compensation of the temperature influences in a miniature three-axial accelerometer based on the least squares method

NASA Astrophysics Data System (ADS)

Grigorie, Teodor Lucian; Corcau, Ileana Jenica; Tudosie, Alexandru Nicolae

2017-06-01

The paper presents a way to obtain an intelligent miniaturized three-axial accelerometric sensor, based on the on-line estimation and compensation of the sensor errors generated by the environmental temperature variation. Taking into account that this error's value is a strongly nonlinear complex function of the values of environmental temperature and of the acceleration exciting the sensor, its correction may not be done off-line and it requires the presence of an additional temperature sensor. The proposed identification methodology for the error model is based on the least square method which process off-line the numerical values obtained from the accelerometer experimental testing for different values of acceleration applied to its axes of sensitivity and for different values of operating temperature. A final analysis of the error level after the compensation highlights the best variant for the matrix in the error model. In the sections of the paper are shown the results of the experimental testing of the accelerometer on all the three sensitivity axes, the identification of the error models on each axis by using the least square method, and the validation of the obtained models with experimental values. For all of the three detection channels was obtained a reduction by almost two orders of magnitude of the acceleration absolute maximum error due to environmental temperature variation.

Effect of satellite formations and imaging modes on global albedo estimation

NASA Astrophysics Data System (ADS)

Nag, Sreeja; Gatebe, Charles K.; Miller, David W.; de Weck, Olivier L.

2016-05-01

We confirm the applicability of using small satellite formation flight for multi-angular earth observation to retrieve global, narrow band, narrow field-of-view albedo. The value of formation flight is assessed using a coupled systems engineering and science evaluation model, driven by Model Based Systems Engineering and Observing System Simulation Experiments. Albedo errors are calculated against bi-directional reflectance data obtained from NASA airborne campaigns made by the Cloud Absorption Radiometer for the seven major surface types, binned using MODIS' land cover map - water, forest, cropland, grassland, snow, desert and cities. A full tradespace of architectures with three to eight satellites, maintainable orbits and imaging modes (collective payload pointing strategies) are assessed. For an arbitrary 4-sat formation, changing the reference, nadir-pointing satellite dynamically reduces the average albedo error to 0.003, from 0.006 found in the static referencecase. Tracking pre-selected waypoints with all the satellites reduces the average error further to 0.001, allows better polar imaging and continued operations even with a broken formation. An albedo error of 0.001 translates to 1.36 W/m2 or 0.4% in Earth's outgoing radiation error. Estimation errors are found to be independent of the satellites' altitude and inclination, if the nadir-looking is changed dynamically. The formation satellites are restricted to differ in only right ascension of planes and mean anomalies within slotted bounds. Three satellites in some specific formations show average albedo errors of less than 2% with respect to airborne, ground data and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%. More than 70% ground spot overlap between the satellites is possible with 0.5° of pointing accuracy, 2 Km of GPS accuracy and commands uplinked once a day. The formations can be maintained at less than 1 m/s of monthly ΔV per satellite.

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies

PubMed Central

Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

2017-01-01

Abstract Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. PMID:29106476

Pennation angle dependency in skeletal muscle tissue doppler strain in dynamic contractions.

PubMed

Lindberg, Frida; Öhberg, Fredrik; Granåsen, Gabriel; Brodin, Lars-Åke; Grönlund, Christer

2011-07-01

Tissue velocity imaging (TVI) is a Doppler based ultrasound technique that can be used to study regional deformation in skeletal muscle tissue. The aim of this study was to develop a biomechanical model to describe the TVI strain's dependency on the pennation angle. We demonstrate its impact as the subsequent strain measurement error using dynamic elbow contractions from the medial and the lateral part of biceps brachii at two different loadings; 5% and 25% of maximum voluntary contraction (MVC). The estimated pennation angles were on average about 4° in extended position and increased to a maximal of 13° in flexed elbow position. The corresponding relative angular error spread from around 7% up to around 40%. To accurately apply TVI on skeletal muscles, the error due to angle changes should be compensated for. As a suggestion, this could be done according to the presented model. Copyright © 2011 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Drag Coefficient Estimation in Orbit Determination

NASA Astrophysics Data System (ADS)

McLaughlin, Craig A.; Manee, Steve; Lichtenberg, Travis

2011-07-01

Drag modeling is the greatest uncertainty in the dynamics of low Earth satellite orbits where ballistic coefficient and density errors dominate drag errors. This paper examines fitted drag coefficients found as part of a precision orbit determination process for Stella, Starlette, and the GEOSAT Follow-On satellites from 2000 to 2005. The drag coefficients for the spherical Stella and Starlette satellites are assumed to be highly correlated with density model error. The results using MSIS-86, NRLMSISE-00, and NRLMSISE-00 with dynamic calibration of the atmosphere (DCA) density corrections are compared. The DCA corrections were formulated for altitudes of 200-600 km and are found to be inappropriate when applied at 800 km. The yearly mean fitted drag coefficients are calculated for each satellite for each year studied. The yearly mean drag coefficients are higher for Starlette than Stella, where Starlette is at a higher altitude. The yearly mean fitted drag coefficients for all three satellites decrease as solar activity decreases after solar maximum.

Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi.

PubMed

Chen, Jing; Zhang, Yi; Xue, Wei

2018-04-28

In this paper, we propose UILoc, an unsupervised indoor localization scheme that uses a combination of smartphone sensors, iBeacons and Wi-Fi fingerprints for reliable and accurate indoor localization with zero labor cost. Firstly, compared with the fingerprint-based method, the UILoc system can build a fingerprint database automatically without any site survey and the database will be applied in the fingerprint localization algorithm. Secondly, since the initial position is vital to the system, UILoc will provide the basic location estimation through the pedestrian dead reckoning (PDR) method. To provide accurate initial localization, this paper proposes an initial localization module, a weighted fusion algorithm combined with a k-nearest neighbors (KNN) algorithm and a least squares algorithm. In UILoc, we have also designed a reliable model to reduce the landmark correction error. Experimental results show that the UILoc can provide accurate positioning, the average localization error is about 1.1 m in the steady state, and the maximum error is 2.77 m.

Grid convergence errors in hemodynamic solution of patient-specific cerebral aneurysms.

PubMed

Hodis, Simona; Uthamaraj, Susheil; Smith, Andrea L; Dennis, Kendall D; Kallmes, David F; Dragomir-Daescu, Dan

2012-11-15

Computational fluid dynamics (CFD) has become a cutting-edge tool for investigating hemodynamic dysfunctions in the body. It has the potential to help physicians quantify in more detail the phenomena difficult to capture with in vivo imaging techniques. CFD simulations in anatomically realistic geometries pose challenges in generating accurate solutions due to the grid distortion that may occur when the grid is aligned with complex geometries. In addition, results obtained with computational methods should be trusted only after the solution has been verified on multiple high-quality grids. The objective of this study was to present a comprehensive solution verification of the intra-aneurysmal flow results obtained on different morphologies of patient-specific cerebral aneurysms. We chose five patient-specific brain aneurysm models with different dome morphologies and estimated the grid convergence errors for each model. The grid convergence errors were estimated with respect to an extrapolated solution based on the Richardson extrapolation method, which accounts for the degree of grid refinement. For four of the five models, calculated velocity, pressure, and wall shear stress values at six different spatial locations converged monotonically, with maximum uncertainty magnitudes ranging from 12% to 16% on the finest grids. Due to the geometric complexity of the fifth model, the grid convergence errors showed oscillatory behavior; therefore, each patient-specific model required its own grid convergence study to establish the accuracy of the analysis. Copyright © 2012 Elsevier Ltd. All rights reserved.

A function space approach to smoothing with applications to model error estimation for flexible spacecraft control

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1981-01-01

A function space approach to smoothing is used to obtain a set of model error estimates inherent in a reduced-order model. By establishing knowledge of inevitable deficiencies in the truncated model, the error estimates provide a foundation for updating the model and thereby improving system performance. The function space smoothing solution leads to a specification of a method for computation of the model error estimates and development of model error analysis techniques for comparison between actual and estimated errors. The paper summarizes the model error estimation approach as well as an application arising in the area of modeling for spacecraft attitude control.

Model error estimation for distributed systems described by elliptic equations

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1983-01-01

A function space approach is used to develop a theory for estimation of the errors inherent in an elliptic partial differential equation model for a distributed parameter system. By establishing knowledge of the inevitable deficiencies in the model, the error estimates provide a foundation for updating the model. The function space solution leads to a specification of a method for computation of the model error estimates and development of model error analysis techniques for comparison between actual and estimated errors. The paper summarizes the model error estimation approach as well as an application arising in the area of modeling for static shape determination of large flexible systems.

An alternative method to estimate zero flow temperature differences for Granier's thermal dissipation technique.

PubMed

Regalado, Carlos M; Ritter, Axel

2007-08-01

Calibration of the Granier thermal dissipation technique for measuring stem sap flow in trees requires determination of the temperature difference (DeltaT) between a heated and an unheated probe when sap flow is zero (DeltaT(max)). Classically, DeltaT(max) has been estimated from the maximum predawn DeltaT, assuming that sap flow is negligible at nighttime. However, because sap flow may continue during the night, the maximum predawn DeltaT value may underestimate the true DeltaT(max). No alternative method has yet been proposed to estimate DeltaT(max) when sap flow is non-zero at night. A sensitivity analysis is presented showing that errors in DeltaT(max) may amplify through sap flux density computations in Granier's approach, such that small amounts of undetected nighttime sap flow may lead to large diurnal sap flux density errors, hence the need for a correct estimate of DeltaT(max). By rearranging Granier's original formula, an optimization method to compute DeltaT(max) from simultaneous measurements of diurnal DeltaT and micrometeorological variables, without assuming that sap flow is negligible at night, is presented. Some illustrative examples are shown for sap flow measurements carried out on individuals of Erica arborea L., which has needle-like leaves, and Myrica faya Ait., a broadleaf species. We show that, although DeltaT(max) values obtained by the proposed method may be similar in some instances to the DeltaT(max) predicted at night, in general the values differ. The procedure presented has the potential of being applied not only to Granier's method, but to other heat-based sap flow systems that require a zero flow calibration, such as the Cermák et al. (1973) heat balance method and the T-max heat pulse system of Green et al. (2003).

Bracketing mid-pliocene sea surface temperature: maximum and minimum possible warming

USGS Publications Warehouse

Dowsett, Harry

2004-01-01

Estimates of sea surface temperature (SST) from ocean cores reveal a warm phase of the Pliocene between about 3.3 and 3.0 Mega-annums (Ma). Pollen records from land based cores and sections, although not as well dated, also show evidence for a warmer climate at about the same time. Increased greenhouse forcing and altered ocean heat transport is the leading candidates for the underlying cause of Pliocene global warmth. However, despite being a period of global warmth, there exists considerable variability within this interval. Two new SST reconstructions have been created to provide a climatological error bar for warm peak phases of the Pliocene. These data represent the maximum and minimum possible warming recorded within the 3.3 to 3.0 Ma interval.

Flat-fielding of Solar Hα Observations Based on the Maximum Correntropy Criterion

NASA Astrophysics Data System (ADS)

Xu, Gao-Gui; Zheng, Sheng; Lin, Gang-Hua; Wang, Xiao-Fan

2016-08-01

The flat-field CCD calibration method of Kuhn et al. (KLL) is an efficient method for flat-fielding. However, since it depends on the minimum of the sum of squares error (SSE), its solution is sensitive to noise, especially non-Gaussian noise. In this paper, a new algorithm is proposed to determine the flat field. The idea is to change the criterion of gain estimate from SSE to the maximum correntropy. The result of a test on simulated data demonstrates that our method has a higher accuracy and a faster convergence than KLL’s and Chae’s. It has been found that the method effectively suppresses noise, especially in the case of typical non-Gaussian noise. And the computing time of our algorithm is the shortest.

Fishery stock assessment of Kiddi shrimp ( Parapenaeopsis stylifera) in the Northern Arabian Sea Coast of Pakistan by using surplus production models

NASA Astrophysics Data System (ADS)

Mohsin, Muhammad; Mu, Yongtong; Memon, Aamir Mahmood; Kalhoro, Muhammad Talib; Shah, Syed Baber Hussain

2017-07-01

Pakistani marine waters are under an open access regime. Due to poor management and policy implications, blind fishing is continued which may result in ecological as well as economic losses. Thus, it is of utmost importance to estimate fishery resources before harvesting. In this study, catch and effort data, 1996-2009, of Kiddi shrimp Parapenaeopsis stylifera fishery from Pakistani marine waters was analyzed by using specialized fishery software in order to know fishery stock status of this commercially important shrimp. Maximum, minimum and average capture production of P. stylifera was observed as 15 912 metric tons (mt) (1997), 9 438 mt (2009) and 11 667 mt/a. Two stock assessment tools viz. CEDA (catch and effort data analysis) and ASPIC (a stock production model incorporating covariates) were used to compute MSY (maximum sustainable yield) of this organism. In CEDA, three surplus production models, Fox, Schaefer and Pella-Tomlinson, along with three error assumptions, log, log normal and gamma, were used. For initial proportion (IP) 0.8, the Fox model computed MSY as 6 858 mt (CV=0.204, R 2 =0.709) and 7 384 mt (CV=0.149, R 2 =0.72) for log and log normal error assumption respectively. Here, gamma error produced minimization failure. Estimated MSY by using Schaefer and Pella-Tomlinson models remained the same for log, log normal and gamma error assumptions i.e. 7 083 mt, 8 209 mt and 7 242 mt correspondingly. The Schafer results showed highest goodness of fit R 2 (0.712) values. ASPIC computed MSY, CV, R 2, F MSY and B MSY parameters for the Fox model as 7 219 mt, 0.142, 0.872, 0.111 and 65 280, while for the Logistic model the computed values remained 7 720 mt, 0.148, 0.868, 0.107 and 72 110 correspondingly. Results obtained have shown that P. stylifera has been overexploited. Immediate steps are needed to conserve this fishery resource for the future and research on other species of commercial importance is urgently needed.

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.

PubMed

Berglund, Lars; Garmo, Hans; Lindbäck, Johan; Svärdsudd, Kurt; Zethelius, Björn

2008-09-30

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations. Copyright (c) 2008 John Wiley & Sons, Ltd.

Lung motion estimation using dynamic point shifting: An innovative model based on a robust point matching algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yi, Jianbing, E-mail: yijianbing8@163.com; Yang, Xuan, E-mail: xyang0520@263.net; Li, Yan-Ran, E-mail: lyran@szu.edu.cn

2015-10-15

Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered atmore » points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the 3000 landmark points of ten cases by the authors’ method are 1.21 and 1.04 mm. In the EMPIRE10 lung registration challenge, the authors’ method ranks 24 of 39. According to the index of the maximum shear stretch, the authors’ method is also efficient to describe the discontinuous motion at the lung boundaries. Conclusions: By establishing the correspondence of the landmark points in the source phase and the other target phases combining shape matching and image intensity matching together, the mismatching issue in the robust point matching algorithm is adequately addressed. The target registration errors are statistically reduced by shifting the virtual target points and target points. The authors’ method with consideration of sliding conditions can effectively estimate the discontinuous motion, and the estimated motion is natural. The primary limitation of the proposed method is that the temporal constraints of the trajectories of voxels are not introduced into the motion model. However, the proposed method provides satisfactory motion information, which results in precise tumor coverage by the radiation dose during radiotherapy.« less

Lung motion estimation using dynamic point shifting: An innovative model based on a robust point matching algorithm.

PubMed

Yi, Jianbing; Yang, Xuan; Chen, Guoliang; Li, Yan-Ran

2015-10-01

Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. The performances of the authors' method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors' method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the 3000 landmark points of ten cases by the authors' method are 1.21 and 1.04 mm. In the EMPIRE10 lung registration challenge, the authors' method ranks 24 of 39. According to the index of the maximum shear stretch, the authors' method is also efficient to describe the discontinuous motion at the lung boundaries. By establishing the correspondence of the landmark points in the source phase and the other target phases combining shape matching and image intensity matching together, the mismatching issue in the robust point matching algorithm is adequately addressed. The target registration errors are statistically reduced by shifting the virtual target points and target points. The authors' method with consideration of sliding conditions can effectively estimate the discontinuous motion, and the estimated motion is natural. The primary limitation of the proposed method is that the temporal constraints of the trajectories of voxels are not introduced into the motion model. However, the proposed method provides satisfactory motion information, which results in precise tumor coverage by the radiation dose during radiotherapy.

Bias in error estimation when using cross-validation for model selection.

PubMed

Varma, Sudhir; Simon, Richard

2006-02-23

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

Parallel computers - Estimate errors caused by imprecise data

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik; Bernat, Andrew; Villa, Elsa; Mariscal, Yvonne

1991-01-01

A new approach to the problem of estimating errors caused by imprecise data is proposed in the context of software engineering. A software device is used to produce an ideal solution to the problem, when the computer is capable of computing errors of arbitrary programs. The software engineering aspect of this problem is to describe a device for computing the error estimates in software terms and then to provide precise numbers with error estimates to the user. The feasibility of the program capable of computing both some quantity and its error estimate in the range of possible measurement errors is demonstrated.

Very Fast Estimation of Epicentral Distance and Magnitude from a Single Three Component Seismic Station Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Ochoa Gutierrez, L. H.; Niño Vasquez, L. F.; Vargas-Jimenez, C. A.

2012-12-01

To minimize adverse effects originated by high magnitude earthquakes, early warning has become a powerful tool to anticipate a seismic wave arrival to an specific location and lets to bring people and government agencies opportune information to initiate a fast response. To do this, a very fast and accurate characterization of the event must be done but this process is often made using seismograms recorded in at least 4 stations where processing time is usually greater than the wave travel time to the interest area, mainly in coarse networks. A faster process can be done if only one three component seismic station is used that is the closest unsaturated station respect to the epicenter. Here we present a Support Vector Regression algorithm which calculates Magnitude and Epicentral Distance using only 5 seconds of signal since P wave onset. This algorithm was trained with 36 records of historical earthquakes where the input were regression parameters of an exponential function estimated by least squares, corresponding to the waveform envelope and the maximum value of the observed waveform for each component in one single station. A 10 fold Cross Validation was applied for a Normalized Polynomial Kernel obtaining the mean absolute error for different exponents and complexity parameters. Magnitude could be estimated with 0.16 of mean absolute error and the distance with an error of 7.5 km for distances within 60 to 120 km. This kind of algorithm is easy to implement in hardware and can be used directly in the field station to make possible the broadcast of estimations of this values to generate fast decisions at seismological control centers, increasing the possibility to have an effective reactiontribute and Descriptors calculator for SVR model training and test

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

NASA Astrophysics Data System (ADS)

Shamshirband, Shahaboddin; Mohammadi, Kasra; Tong, Chong Wen; Zamani, Mazdak; Motamedi, Shervin; Ch, Sudheer

2016-07-01

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination ( R 2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

PubMed

Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

2017-11-01

Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

TU-F-17A-05: Calculating Tumor Trajectory and Dose-Of-The-Day for Highly Mobile Tumors Using Cone-Beam CT Projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, B; Miften, M

2014-06-15

Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in real-time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to determine the trajectory and dose of highly mobile tumors during each fraction of treatment. Methods: CBCT images of a respiration phantom were acquired, where the trajectory mimicked a lung tumor with high amplitude (2.4 cm) and hysteresis. A template-matching algorithm was used to identify the location of a steel BB in each projection. A Gaussian probability density function for tumor position was calculated which best fit the observed trajectory ofmore » the BB in the imager geometry. Two methods to improve the accuracy of tumor track reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation, and second, using the Monte Carlo method to sample the estimated Gaussian tumor position distribution. 15 clinically-drawn abdominal/lung CTV volumes were used to evaluate the accuracy of the proposed methods by comparing the known and calculated BB trajectories. Results: With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root-mean-square (RMS) trajectory errors were lower than 5% of marker amplitude. Use of respiratory phase information decreased RMS errors by 30%, and decreased the fraction of large errors (>3 mm) by half. Mean dose to the clinical volumes was calculated with an average error of 0.1% and average absolute error of 0.3%. Dosimetric parameters D90/D95 were determined within 0.5% of maximum dose. Monte-Carlo sampling increased RMS trajectory and dosimetric errors slightly, but prevented over-estimation of dose in trajectories with high noise. Conclusions: Tumor trajectory and dose-of-the-day were accurately calculated using CBCT projections. This technique provides a widely-available method to evaluate highly-mobile tumors, and could facilitate better strategies to mitigate or compensate for motion during SBRT.« less

Accuracy of measurement in electrically evoked compound action potentials.

PubMed

Hey, Matthias; Müller-Deile, Joachim

2015-01-15

Electrically evoked compound action potentials (ECAP) in cochlear implant (CI) patients are characterized by the amplitude of the N1P1 complex. The measurement of evoked potentials yields a combination of the measured signal with various noise components but for ECAP procedures performed in the clinical routine, only the averaged curve is accessible. To date no detailed analysis of error dimension has been published. The aim of this study was to determine the error of the N1P1 amplitude and to determine the factors that impact the outcome. Measurements were performed on 32 CI patients with either CI24RE (CA) or CI512 implants using the Software Custom Sound EP (Cochlear). N1P1 error approximation of non-averaged raw data consisting of recorded single-sweeps was compared to methods of error approximation based on mean curves. The error approximation of the N1P1 amplitude using averaged data showed comparable results to single-point error estimation. The error of the N1P1 amplitude depends on the number of averaging steps and amplification; in contrast, the error of the N1P1 amplitude is not dependent on the stimulus intensity. Single-point error showed smaller N1P1 error and better coincidence with 1/√(N) function (N is the number of measured sweeps) compared to the known maximum-minimum criterion. Evaluation of N1P1 amplitude should be accompanied by indication of its error. The retrospective approximation of this measurement error from the averaged data available in clinically used software is possible and best done utilizing the D-trace in forward masking artefact reduction mode (no stimulation applied and recording contains only the switch-on-artefact). Copyright © 2014 Elsevier B.V. All rights reserved.

Two-step reconstruction method using global optimization and conjugate gradient for ultrasound-guided diffuse optical tomography.

PubMed

Tavakoli, Behnoosh; Zhu, Quing

2013-01-01

Ultrasound-guided diffuse optical tomography (DOT) is a promising method for characterizing malignant and benign lesions in the female breast. We introduce a new two-step algorithm for DOT inversion in which the optical parameters are estimated with the global optimization method, genetic algorithm. The estimation result is applied as an initial guess to the conjugate gradient (CG) optimization method to obtain the absorption and scattering distributions simultaneously. Simulations and phantom experiments have shown that the maximum absorption and reduced scattering coefficients are reconstructed with less than 10% and 25% errors, respectively. This is in contrast with the CG method alone, which generates about 20% error for the absorption coefficient and does not accurately recover the scattering distribution. A new measure of scattering contrast has been introduced to characterize benign and malignant breast lesions. The results of 16 clinical cases reconstructed with the two-step method demonstrates that, on average, the absorption coefficient and scattering contrast of malignant lesions are about 1.8 and 3.32 times higher than the benign cases, respectively.

Smartphone-Based Real-Time Indoor Location Tracking With 1-m Precision.

PubMed

Liang, Po-Chou; Krause, Paul

2016-05-01

Monitoring the activities of daily living of the elderly at home is widely recognized as useful for the detection of new or deteriorating health conditions. However, the accuracy of existing indoor location tracking systems remains unsatisfactory. The aim of this study was, therefore, to develop a localization system that can identify a patient's real-time location in a home environment with maximum estimation error of 2 m at a 95% confidence level. A proof-of-concept system based on a sensor fusion approach was built with considerations for lower cost, reduced intrusiveness, and higher mobility, deployability, and portability. This involved the development of both a step detector using the accelerometer and compass of an iPhone 5, and a radio-based localization subsystem using a Kalman filter and received signal strength indication to tackle issues that had been identified as limiting accuracy. The results of our experiments were promising with an average estimation error of 0.47 m. We are confident that with the proposed future work, our design can be adapted to a home-like environment with a more robust localization solution.

An improved method for predicting the evolution of the characteristic parameters of an information system

NASA Astrophysics Data System (ADS)

Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

2018-03-01

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

Adaptive OFDM Radar Waveform Design for Improved Micro-Doppler Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

Here we analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a rotating target having multiple scattering centers. The use of a frequency-diverse OFDM signal enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. We characterize the accuracy of micro-Doppler frequency estimation by computing the Cramer-Rao bound (CRB) on the angular-velocity estimate of the target. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to themore » OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations with respect to the signal-to-noise ratios, number of temporal samples, and number of OFDM subcarriers. We also analysed numerically the improvement in estimation accuracy due to the adaptive waveform design. A grid-based maximum likelihood estimation technique is applied to evaluate the corresponding mean-squared error performance.« less

Estimation of Power Consumption in the Circular Sawing of Stone Based on Tangential Force Distribution

NASA Astrophysics Data System (ADS)

Huang, Guoqin; Zhang, Meiqin; Huang, Hui; Guo, Hua; Xu, Xipeng

2018-04-01

Circular sawing is an important method for the processing of natural stone. The ability to predict sawing power is important in the optimisation, monitoring and control of the sawing process. In this paper, a predictive model (PFD) of sawing power, which is based on the tangential force distribution at the sawing contact zone, was proposed, experimentally validated and modified. With regard to the influence of sawing speed on tangential force distribution, the modified PFD (MPFD) performed with high predictive accuracy across a wide range of sawing parameters, including sawing speed. The mean maximum absolute error rate was within 6.78%, and the maximum absolute error rate was within 11.7%. The practicability of predicting sawing power by the MPFD with few initial experimental samples was proved in case studies. On the premise of high sample measurement accuracy, only two samples are required for a fixed sawing speed. The feasibility of applying the MPFD to optimise sawing parameters while lowering the energy consumption of the sawing system was validated. The case study shows that energy use was reduced 28% by optimising the sawing parameters. The MPFD model can be used to predict sawing power, optimise sawing parameters and control energy.

Long-range wind monitoring in real time with optimized coherent lidar

NASA Astrophysics Data System (ADS)

Dolfi-Bouteyre, Agnes; Canat, Guillaume; Lombard, Laurent; Valla, Matthieu; Durécu, Anne; Besson, Claudine

2017-03-01

Two important enabling technologies for pulsed coherent detection wind lidar are the laser and real-time signal processing. In particular, fiber laser is limited in peak power by nonlinear effects, such as stimulated Brillouin scattering (SBS). We report on various technologies that have been developed to mitigate SBS and increase peak power in 1.5-μm fiber lasers, such as special large mode area fiber designs or strain management. Range-resolved wind profiles up to a record range of 16 km within 0.1-s averaging time have been obtained thanks to those high-peak power fiber lasers. At long range, the lidar signal gets much weaker than the noise and special care is required to extract the Doppler peak from the spectral noise. To optimize real-time processing for weak carrier-to-noise ratio signal, we have studied various Doppler mean frequency estimators (MFE) and the influence of data accumulation on outliers occurrence. Five real-time MFEs (maximum, centroid, matched filter, maximum likelihood, and polynomial fit) have been compared in terms of error and processing time using lidar experimental data. MFE errors and data accumulation limits are established using a spectral method.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter

NASA Astrophysics Data System (ADS)

Wang, Yujie; Zhang, Xu; Liu, Chang; Pan, Rui; Chen, Zonghai

2018-06-01

The power capability and maximum charge and discharge energy are key indicators for energy management systems, which can help the energy storage devices work in a suitable area and prevent them from over-charging and over-discharging. In this work, a model based power and energy assessment approach is proposed for the lithium-ion battery and supercapacitor hybrid system. The model framework of the lithium-ion battery and supercapacitor hybrid system is developed based on the equivalent circuit model, and the model parameters are identified by regression method. Explicit analyses of the power capability and maximum charge and discharge energy prediction with multiple constraints are elaborated. Subsequently, the extended Kalman filter is employed for on-board power capability and maximum charge and discharge energy prediction to overcome estimation error caused by system disturbance and sensor noise. The charge and discharge power capability, and the maximum charge and discharge energy are quantitatively assessed under both the dynamic stress test and the urban dynamometer driving schedule. The maximum charge and discharge energy prediction of the lithium-ion battery and supercapacitor hybrid system with different time scales are explored and discussed.

Applications of non-standard maximum likelihood techniques in energy and resource economics

NASA Astrophysics Data System (ADS)

Moeltner, Klaus

Two important types of non-standard maximum likelihood techniques, Simulated Maximum Likelihood (SML) and Pseudo-Maximum Likelihood (PML), have only recently found consideration in the applied economic literature. The objective of this thesis is to demonstrate how these methods can be successfully employed in the analysis of energy and resource models. Chapter I focuses on SML. It constitutes the first application of this technique in the field of energy economics. The framework is as follows: Surveys on the cost of power outages to commercial and industrial customers usually capture multiple observations on the dependent variable for a given firm. The resulting pooled data set is censored and exhibits cross-sectional heterogeneity. We propose a model that addresses these issues by allowing regression coefficients to vary randomly across respondents and by using the Geweke-Hajivassiliou-Keane simulator and Halton sequences to estimate high-order cumulative distribution terms. This adjustment requires the use of SML in the estimation process. Our framework allows for a more comprehensive analysis of outage costs than existing models, which rely on the assumptions of parameter constancy and cross-sectional homogeneity. Our results strongly reject both of these restrictions. The central topic of the second Chapter is the use of PML, a robust estimation technique, in count data analysis of visitor demand for a system of recreation sites. PML has been popular with researchers in this context, since it guards against many types of mis-specification errors. We demonstrate, however, that estimation results will generally be biased even if derived through PML if the recreation model is based on aggregate, or zonal data. To countervail this problem, we propose a zonal model of recreation that captures some of the underlying heterogeneity of individual visitors by incorporating distributional information on per-capita income into the aggregate demand function. This adjustment eliminates the unrealistic constraint of constant income across zonal residents, and thus reduces the risk of aggregation bias in estimated macro-parameters. The corrected aggregate specification reinstates the applicability of PML. It also increases model efficiency, and allows-for the generation of welfare estimates for population subgroups.

Estimation of Gutenberg-Richter b-value using instrumental earthquake catalog from the southern Korean Peninsula

NASA Astrophysics Data System (ADS)

Lee, H.; Sheen, D.; Kim, S.

2013-12-01

The b-value in Gutenberg-Richter relation is an important parameter widely used not only in the interpretation of regional tectonic structure but in the seismic hazard analysis. In this study, we tested four methods for estimating the stable b-value in a small number of events using Monte-Carlo method. One is the Least-Squares method (LSM) which minimizes the observation error. Others are based on the Maximum Likelihood method (MLM) which maximizes the likelihood function: Utsu's (1965) method for continuous magnitudes and an infinite maximum magnitude, Page's (1968) for continuous magnitudes and a finite maximum magnitude, and Weichert's (1980) for interval magnitude and a finite maximum magnitude. A synthetic parent population of the earthquake catalog of million events from magnitude 2.0 to 7.0 with interval of 0.1 was generated for the Monte-Carlo simulation. The sample, the number of which was increased from 25 to 1000, was extracted from the parent population randomly. The resampling procedure was applied 1000 times with different random seed numbers. The mean and the standard deviation of the b-value were estimated for each sample group that has the same number of samples. As expected, the more samples were used, the more stable b-value was obtained. However, in a small number of events, the LSM gave generally low b-value with a large standard deviation while other MLMs gave more accurate and stable values. It was found that Utsu (1965) gives the most accurate and stable b-value even in a small number of events. It was also found that the selection of the minimum magnitude could be critical for estimating the correct b-value for Utsu's (1965) method and Page's (1968) if magnitudes were binned into an interval. Therefore, we applied Utsu (1965) to estimate the b-value using two instrumental earthquake catalogs, which have events occurred around the southern part of the Korean Peninsula from 1978 to 2011. By a careful choice of the minimum magnitude, the b-values of the earthquake catalogs of the Korea Meteorological Administration and Kim (2012) are estimated to be 0.72 and 0.74, respectively.

Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

NASA Technical Reports Server (NTRS)

da Silva, Arlindo

2010-01-01

A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of assimilating AOD retrievals from MODIS (on both Aqua and TERRA satellites) from AERONET for validation. The impact on the GEOS-5 Aerosol Forecasting will be fully documented.

PAPER-CHROMATOGRAM MEASUREMENT OF SUBSTANCES LABELLED WITH H$sup 3$ (in German)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wenzel, M.

1961-03-01

Compounds labelled with H/sup 3/ can be detected with a paper chromatogram using a methane flow counter with a count yield of 1%. The yield can be estimated from the beta maximum energy. A new double counter was developed which increases the count yield to 2% and also considerably decreases the margin of error. Calibration curves with leucine and glucosamine show satisfactory linearity between measured and applied activity in the range from 4 to 50 x 10/sup -//sup 3/ mu c of H/sup 3/. (auth)

A comparison of two estimates of standard error for a ratio-of-means estimator for a mapped-plot sample design in southeast Alaska.

Treesearch

Willem W.S. van Hees

2002-01-01

Comparisons of estimated standard error for a ratio-of-means (ROM) estimator are presented for forest resource inventories conducted in southeast Alaska between 1995 and 2000. Estimated standard errors for the ROM were generated by using a traditional variance estimator and also approximated by bootstrap methods. Estimates of standard error generated by both...

Floating-point system quantization errors in digital control systems

NASA Technical Reports Server (NTRS)

Phillips, C. L.

1973-01-01

The results are reported of research into the effects on system operation of signal quantization in a digital control system. The investigation considered digital controllers (filters) operating in floating-point arithmetic in either open-loop or closed-loop systems. An error analysis technique is developed, and is implemented by a digital computer program that is based on a digital simulation of the system. As an output the program gives the programing form required for minimum system quantization errors (either maximum of rms errors), and the maximum and rms errors that appear in the system output for a given bit configuration. The program can be integrated into existing digital simulations of a system.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality.

PubMed

Yang, Lei; Qin, Guoyou; Zhao, Naiqing; Wang, Chunfang; Song, Guixiang

2012-10-30

Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton's method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality

PubMed Central

2012-01-01

Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Methods Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton’s method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. Results In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. Conclusions GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies. PMID:23110601

Temperature-based estimation of global solar radiation using soft computing methodologies

NASA Astrophysics Data System (ADS)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Danesh, Amir Seyed; Abdullah, Mohd Shahidan; Zamani, Mazdak

2016-07-01

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures ( T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max- T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

Estimating population diversity with CatchAll

PubMed Central

Bunge, John; Woodard, Linda; Böhning, Dankmar; Foster, James A.; Connolly, Sean; Allen, Heather K.

2012-01-01

Motivation: The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or species richness in a population. To date, only relatively simple methods have been implemented in available software. There is a need for software employing modern, computationally intensive statistical analyses including error, goodness-of-fit and robustness assessments. Results: We present CatchAll, a fast, easy-to-use, platform-independent program that computes maximum likelihood estimates for finite-mixture models, weighted linear regression-based analyses and coverage-based non-parametric methods, along with outlier diagnostics. Given sample ‘frequency count’ data, CatchAll computes 12 different diversity estimates and applies a model-selection algorithm. CatchAll also derives discounted diversity estimates to adjust for possibly uncertain low-frequency counts. It is accompanied by an Excel-based graphics program. Availability: Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall. Contact: jab18@cornell.edu PMID:22333246

On the more accurate channel model and positioning based on time-of-arrival for visible light localization

NASA Astrophysics Data System (ADS)

Amini, Changeez; Taherpour, Abbas; Khattab, Tamer; Gazor, Saeed

2017-01-01

This paper presents an improved propagation channel model for the visible light in indoor environments. We employ this model to derive an enhanced positioning algorithm using on the relation between the time-of-arrivals (TOAs) and the distances for two cases either by assuming known or unknown transmitter and receiver vertical distances. We propose two estimators, namely the maximum likelihood estimator and an estimator by employing the method of moments. To have an evaluation basis for these methods, we calculate the Cramer-Rao lower bound (CRLB) for the performance of the estimations. We show that the proposed model and estimations result in a superior performance in positioning when the transmitter and receiver are perfectly synchronized in comparison to the existing state-of-the-art counterparts. Moreover, the corresponding CRLB of the proposed model represents almost about 20 dB reduction in the localization error bound in comparison with the previous model for some practical scenarios.

Change-in-ratio estimators for populations with more than two subclasses

USGS Publications Warehouse

Udevitz, Mark S.; Pollock, Kenneth H.

1991-01-01

Change-in-ratio methods have been developed to estimate the size of populations with two or three population subclasses. Most of these methods require the often unreasonable assumption of equal sampling probabilities for individuals in all subclasses. This paper presents new models based on the weaker assumption that ratios of sampling probabilities are constant over time for populations with three or more subclasses. Estimation under these models requires that a value be assumed for one of these ratios when there are two samples. Explicit expressions are given for the maximum likelihood estimators under models for two samples with three or more subclasses and for three samples with two subclasses. A numerical method using readily available statistical software is described for obtaining the estimators and their standard errors under all of the models. Likelihood ratio tests that can be used in model selection are discussed. Emphasis is on the two-sample, three-subclass models for which Monte-Carlo simulation results and an illustrative example are presented.

A-posteriori error estimation for second order mechanical systems

NASA Astrophysics Data System (ADS)

Ruiner, Thomas; Fehr, Jörg; Haasdonk, Bernard; Eberhard, Peter

2012-06-01

One important issue for the simulation of flexible multibody systems is the reduction of the flexible bodies degrees of freedom. As far as safety questions are concerned knowledge about the error introduced by the reduction of the flexible degrees of freedom is helpful and very important. In this work, an a-posteriori error estimator for linear first order systems is extended for error estimation of mechanical second order systems. Due to the special second order structure of mechanical systems, an improvement of the a-posteriori error estimator is achieved. A major advantage of the a-posteriori error estimator is that the estimator is independent of the used reduction technique. Therefore, it can be used for moment-matching based, Gramian matrices based or modal based model reduction techniques. The capability of the proposed technique is demonstrated by the a-posteriori error estimation of a mechanical system, and a sensitivity analysis of the parameters involved in the error estimation process is conducted.

Standard free energy of formation of iron iodide

NASA Technical Reports Server (NTRS)

Khandkar, A.; Tare, V. B.; Wagner, J. B., Jr.

1983-01-01

An experiment is reported where silver iodide is used to determine the standard free energy of formation of iron iodide. By using silver iodide as a solid electrolyte, a galvanic cell, Ag/AgI/Fe-FeI2, is formulated. The standard free energy of formation of AgI is known, and hence it is possible to estimate the standard free energy of formation of FeI2 by measuring the open-circuit emf of the above cell as a function of temperature. The free standard energy of formation of FeI2 determined by this method is -38784 + 24.165T cal/mol. It is estimated that the maximum error associated with this method is plus or minus 2500 cal/mol.

Optimal estimation of large structure model errors. [in Space Shuttle controller design

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1979-01-01

In-flight estimation of large structure model errors is usually required as a means of detecting inevitable deficiencies in large structure controller/estimator models. The present paper deals with a least-squares formulation which seeks to minimize a quadratic functional of the model errors. The properties of these error estimates are analyzed. It is shown that an arbitrary model error can be decomposed as the sum of two components that are orthogonal in a suitably defined function space. Relations between true and estimated errors are defined. The estimates are found to be approximations that retain many of the significant dynamics of the true model errors. Current efforts are directed toward application of the analytical results to a reference large structure model.

Efficient design and inference for multistage randomized trials of individualized treatment policies.

PubMed

Dawson, Ree; Lavori, Philip W

2012-01-01

Clinical demand for individualized "adaptive" treatment policies in diverse fields has spawned development of clinical trial methodology for their experimental evaluation via multistage designs, building upon methods intended for the analysis of naturalistically observed strategies. Because often there is no need to parametrically smooth multistage trial data (in contrast to observational data for adaptive strategies), it is possible to establish direct connections among different methodological approaches. We show by algebraic proof that the maximum likelihood (ML) and optimal semiparametric (SP) estimators of the population mean of the outcome of a treatment policy and its standard error are equal under certain experimental conditions. This result is used to develop a unified and efficient approach to design and inference for multistage trials of policies that adapt treatment according to discrete responses. We derive a sample size formula expressed in terms of a parametric version of the optimal SP population variance. Nonparametric (sample-based) ML estimation performed well in simulation studies, in terms of achieved power, for scenarios most likely to occur in real studies, even though sample sizes were based on the parametric formula. ML outperformed the SP estimator; differences in achieved power predominately reflected differences in their estimates of the population mean (rather than estimated standard errors). Neither methodology could mitigate the potential for overestimated sample sizes when strong nonlinearity was purposely simulated for certain discrete outcomes; however, such departures from linearity may not be an issue for many clinical contexts that make evaluation of competitive treatment policies meaningful.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-03-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-05-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

From least squares to multilevel modeling: A graphical introduction to Bayesian inference

NASA Astrophysics Data System (ADS)

Loredo, Thomas J.

2016-01-01

This tutorial presentation will introduce some of the key ideas and techniques involved in applying Bayesian methods to problems in astrostatistics. The focus will be on the big picture: understanding the foundations (interpreting probability, Bayes's theorem, the law of total probability and marginalization), making connections to traditional methods (propagation of errors, least squares, chi-squared, maximum likelihood, Monte Carlo simulation), and highlighting problems where a Bayesian approach can be particularly powerful (Poisson processes, density estimation and curve fitting with measurement error). The "graphical" component of the title reflects an emphasis on pictorial representations of some of the math, but also on the use of graphical models (multilevel or hierarchical models) for analyzing complex data. Code for some examples from the talk will be available to participants, in Python and in the Stan probabilistic programming language.

A comparative study of optimum and suboptimum direct-detection laser ranging receivers

NASA Technical Reports Server (NTRS)

Abshire, J. B.

1978-01-01

A summary of previously proposed receiver strategies for direct-detection laser ranging receivers is presented. Computer simulations are used to compare performance of candidate implementation strategies in the 1- to 100-photoelectron region. Under the condition of no background radiation, the maximum-likelihood and minimum mean-square error estimators were found to give the same performance for both bell-shaped and rectangular optical-pulse shapes. For signal energies greater than 100 photoelectrons, the root-mean-square range error is shown to decrease as Q to the -1/2 power for bell-shaped pulses and Q to the -1 power for rectangular pulses, where Q represents the average pulse energy. Of several receiver implementations presented, the matched-filter peak detector was found to be preferable. A similar configuration, using a constant-fraction discriminator, exhibited a signal-level dependent time bias.

TDRS orbit determination by radio interferometry

NASA Technical Reports Server (NTRS)

Pavloff, Michael S.

1994-01-01

In support of a NASA study on the application of radio interferometry to satellite orbit determination, MITRE developed a simulation tool for assessing interferometry tracking accuracy. The Orbit Determination Accuracy Estimator (ODAE) models the general batch maximum likelihood orbit determination algorithms of the Goddard Trajectory Determination System (GTDS) with the group and phase delay measurements from radio interferometry. ODAE models the statistical properties of tracking error sources, including inherent observable imprecision, atmospheric delays, clock offsets, station location uncertainty, and measurement biases, and through Monte Carlo simulation, ODAE calculates the statistical properties of errors in the predicted satellites state vector. This paper presents results from ODAE application to orbit determination of the Tracking and Data Relay Satellite (TDRS) by radio interferometry. Conclusions about optimal ground station locations for interferometric tracking of TDRS are presented, along with a discussion of operational advantages of radio interferometry.

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

PubMed

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

Occupancy Modeling Species-Environment Relationships with Non-ignorable Survey Designs.

PubMed

Irvine, Kathryn M; Rodhouse, Thomas J; Wright, Wilson J; Olsen, Anthony R

2018-05-26

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if datasets from probability and non-probability surveys are combined or unequal selection probabilities are used, the design may be non ignorable. We outline the use of pseudo-maximum likelihood estimation for site-occupancy models to account for such non-ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo-likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non-detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling datasets composed of sites contributed outside of a probability design Pseudo-maximum likelihood estimates differed from the usual maximum likelihood occu31 pancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species-environment relationships with non-ignorable sampling designs was biased, whereas the pseudo-likelihood estimator was design-unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non-probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and 4 revisits). Aggregating datasets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model-based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence-based conservation decision-making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems.

PubMed

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng

2018-05-31

For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are useful methods to solve the state estimation problems, and both can obtain good performance in Gaussian noises. However, their performances often degrade significantly in the face of non-Gaussian noises, particularly when the measurements are contaminated by some heavy-tailed impulsive noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance instead of traditional minimum mean square error (MMSE) criterion, a new square-root nonlinear filter is proposed in this study, named as the maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filter not only retains the advantage of square-root cubature Kalman filter (SCKF), but also exhibits robust performance against heavy-tailed non-Gaussian noises. A judgment condition that avoids numerical problem is also given. The results of two illustrative examples, especially the SINS/GPS integrated systems, demonstrate the desirable performance of the proposed filter. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Riishojgaard, Lars Peter; Rood, Richard B.

2000-01-01

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar

PubMed Central

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-01

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R2 = 0.340, root-mean-square error (RMSE) = 81.89 g·m−2, and relative error of 14.1%). The improvement of multiple regressions to the R2 and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns. PMID:28106819

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

PubMed

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-19

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass ( R ² = 0.340, root-mean-square error (RMSE) = 81.89 g·m -2 , and relative error of 14.1%). The improvement of multiple regressions to the R ² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

Evaluation of dynamic balance among community-dwelling older adult fallers: a generalizability study of the limits of stability test.

PubMed

Clark, S; Rose, D J

2001-04-01

To establish reliability estimates of the 75% Limits of Stability Test (75% LOS test) when administered to community-dwelling older adults with a history of falls. Generalizability theory was used to estimate both the relative contribution of identified error sources to the total measurement error and generalizability coefficients. A random effects repeated-measures analysis of variance (ANOVA) was used to assess consistency of LOS test movement variables across both days and targets. A motor control research laboratory in a university setting. Fifty community-dwelling older adults with 2 or more falls in the previous year. Spatial and temporal measures of dynamic balance derived from the 75% LOS test included average movement velocity, maximum center of gravity (COG) excursion, end-point COG excursion, and directional control. Estimated generalizability coefficients for 2 testing days ranged from.58 to.87. Total variance in LOS test measures attributable to inconsistencies in day-to-day test performance (Day and Subject x Day facets) ranged from 2.5% to 8.4%. The ANOVA results indicated that no significant differences were observed in the LOS test variables across the 2 testing days. The 75% LOS test administered to older adult fallers on 2 consecutive days provides consistent and reliable measures of dynamic balance.

Star Tracker Performance Estimate with IMU

NASA Technical Reports Server (NTRS)

Aretskin-Hariton, Eliot D.; Swank, Aaron J.

2015-01-01

A software tool for estimating cross-boresight error of a star tracker combined with an inertial measurement unit (IMU) was developed to support trade studies for the Integrated Radio and Optical Communication project (iROC) at the National Aeronautics and Space Administration Glenn Research Center. Typical laser communication systems, such as the Lunar Laser Communication Demonstration (LLCD) and the Laser Communication Relay Demonstration (LCRD), use a beacon to locate ground stations. iROC is investigating the use of beaconless precision laser pointing to enable laser communication at Mars orbits and beyond. Precision attitude knowledge is essential to the iROC mission to enable high-speed steering of the optical link. The preliminary concept to achieve this precision attitude knowledge is to use star trackers combined with an IMU. The Star Tracker Accuracy (STAcc) software was developed to rapidly assess the capabilities of star tracker and IMU configurations. STAcc determines the overall cross-boresight error of a star tracker with an IMU given the characteristic parameters: quantum efficiency, aperture, apparent star magnitude, exposure time, field of view, photon spread, detector pixels, spacecraft slew rate, maximum stars used for quaternion estimation, and IMU angular random walk. This paper discusses the supporting theory used to construct STAcc, verification of the program and sample results.

Models for estimating daily rainfall erosivity in China

NASA Astrophysics Data System (ADS)

Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Nearing, Mark A.; Zhao, Ying

2016-04-01

The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha-1 h-1 y-1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency for daily erosivity index prediction of 0.93. Thus daily rainfall data was generally sufficient for estimating annual average, yearly, and half-monthly time scales, while sub-daily data was needed when estimating daily erosivity values.

Simulating the effect of non-linear mode coupling in cosmological parameter estimation

NASA Astrophysics Data System (ADS)

Kiessling, A.; Taylor, A. N.; Heavens, A. F.

2011-09-01

Fisher Information Matrix methods are commonly used in cosmology to estimate the accuracy that cosmological parameters can be measured with a given experiment and to optimize the design of experiments. However, the standard approach usually assumes both data and parameter estimates are Gaussian-distributed. Further, for survey forecasts and optimization it is usually assumed that the power-spectrum covariance matrix is diagonal in Fourier space. However, in the low-redshift Universe, non-linear mode coupling will tend to correlate small-scale power, moving information from lower to higher order moments of the field. This movement of information will change the predictions of cosmological parameter accuracy. In this paper we quantify this loss of information by comparing naïve Gaussian Fisher matrix forecasts with a maximum likelihood parameter estimation analysis of a suite of mock weak lensing catalogues derived from N-body simulations, based on the SUNGLASS pipeline, for a 2D and tomographic shear analysis of a Euclid-like survey. In both cases, we find that the 68 per cent confidence area of the Ωm-σ8 plane increases by a factor of 5. However, the marginal errors increase by just 20-40 per cent. We propose a new method to model the effects of non-linear shear-power mode coupling in the Fisher matrix by approximating the shear-power distribution as a multivariate Gaussian with a covariance matrix derived from the mock weak lensing survey. We find that this approximation can reproduce the 68 per cent confidence regions of the full maximum likelihood analysis in the Ωm-σ8 plane to high accuracy for both 2D and tomographic weak lensing surveys. Finally, we perform a multiparameter analysis of Ωm, σ8, h, ns, w0 and wa to compare the Gaussian and non-linear mode-coupled Fisher matrix contours. The 6D volume of the 1σ error contours for the non-linear Fisher analysis is a factor of 3 larger than for the Gaussian case, and the shape of the 68 per cent confidence volume is modified. We propose that future Fisher matrix estimates of cosmological parameter accuracies should include mode-coupling effects.

A MAD Explanation for the Correlation between Bulk Lorentz Factor and Minimum Variability Timescale

NASA Astrophysics Data System (ADS)

Lloyd-Ronning, Nicole; Lei, Wei-hua; Xie, Wei

2018-04-01

We offer an explanation for the anti-correlation between the minimum variability timescale (MTS) in the prompt emission light curve of gamma-ray bursts (GRBs) and the estimated bulk Lorentz factor of these GRBs, in the context of a magnetically arrested disk (MAD) model. In particular, we show that previously derived limits on the maximum available energy per baryon in a Blandford-Znajek jet leads to a relationship between the characteristic MAD timescale in GRBs and the maximum bulk Lorentz factor: tMAD∝Γ-6, somewhat steeper than (although within the error bars of) the fitted relationship found in the GRB data. Similarly, the MAD model also naturally accounts for the observed anti-correlation between MTS and gamma-ray luminosity L in the GRB data, and we estimate the accretion rates of the GRB disk (given these luminosities) in the context of this model. Both of these correlations (MTS - Γ and MTS - L) are also observed in the AGN data, and we discuss the implications of our results in the context of both GRB and blazar systems.

A new method for evaluating impacts of data assimilation with respect to tropical cyclone intensity forecast problem

NASA Astrophysics Data System (ADS)

Vukicevic, T.; Uhlhorn, E.; Reasor, P.; Klotz, B.

2012-12-01

A significant potential for improving numerical model forecast skill of tropical cyclone (TC) intensity by assimilation of airborne inner core observations in high resolution models has been demonstrated in recent studies. Although encouraging , the results so far have not provided clear guidance on the critical information added by the inner core data assimilation with respect to the intensity forecast skill. Better understanding of the relationship between the intensity forecast and the value added by the assimilation is required to further the progress, including the assimilation of satellite observations. One of the major difficulties in evaluating such a relationship is the forecast verification metric of TC intensity: the maximum one-minute sustained wind speed at 10 m above surface. The difficulty results from two issues : 1) the metric refers to a practically unobservable quantity since it is an extreme value in a highly turbulent, and spatially-extensive wind field and 2) model- and observation-based estimates of this measure are not compatible in terms of spatial and temporal scales, even in high-resolution models. Although the need for predicting the extreme value of near surface wind is well justified, and the observation-based estimates that are used in practice are well thought of, a revised metric for the intensity is proposed for the purpose of numerical forecast evaluation and the impacts on the forecast. The metric should enable a robust observation- and model-resolvable and phenomenologically-based evaluation of the impacts. It is shown that the maximum intensity could be represented in terms of decomposition into deterministic and stochastic components of the wind field. Using the vortex-centric cylindrical reference frame, the deterministic component is defined as the sum of amplitudes of azimuthal wave numbers 0 and 1 at the radius of maximum wind, whereas the stochastic component is represented by a non-Gaussian PDF. This decomposition is exact and fully independent of individual TC properties. The decomposition of the maximum wind intensity was first evaluated using several sources of data including Step Frequency Microwave Radiometer surface wind speeds from NOAA and Air Force reconnaissance flights,NOAA P-3 Tail Doppler Radar measurements, and best track maximum intensity estimates as well as the simulations from Hurricane WRF Ensemble Data Assimilation System (HEDAS) experiments for 83 real data cases. The results confirmed validity of the method: the stochastic component of the maximum exibited a non-Gaussian PDF with small mean amplitude and variance that was comparable to the known best track error estimates. The results of the decomposition were then used to evaluate the impact of the improved initial conditions on the forecast. It was shown that the errors in the deterministic component of the intensity had the dominant effect on the forecast skill for the studied cases. This result suggests that the data assimilation of the inner core observations could focus primarily on improving the analysis of wave number 0 and 1 initial structure and on the mechanisms responsible for forcing the evolution of this low-wavenumber structure. For the latter analysis, the assimilation of airborne and satellite remote sensing observations could play significant role.

Accounting for misclassification error in retrospective smoking data.

PubMed

Kenkel, Donald S; Lillard, Dean R; Mathios, Alan D

2004-10-01

Recent waves of major longitudinal surveys in the US and other countries include retrospective questions about the timing of smoking initiation and cessation, creating a potentially important but under-utilized source of information on smoking behavior over the life course. In this paper, we explore the extent of, consequences of, and possible solutions to misclassification errors in models of smoking participation that use data generated from retrospective reports. In our empirical work, we exploit the fact that the National Longitudinal Survey of Youth 1979 provides both contemporaneous and retrospective information about smoking status in certain years. We compare the results from four sets of models of smoking participation. The first set of results are from baseline probit models of smoking participation from contemporaneously reported information. The second set of results are from models that are identical except that the dependent variable is based on retrospective information. The last two sets of results are from models that take a parametric approach to account for a simple form of misclassification error. Our preliminary results suggest that accounting for misclassification error is important. However, the adjusted maximum likelihood estimation approach to account for misclassification does not always perform as expected. Copyright 2004 John Wiley & Sons, Ltd.

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Bayesian inference of Calibration curves: application to archaeomagnetism

NASA Astrophysics Data System (ADS)

Lanos, P.

2003-04-01

The range of errors that occur at different stages of the archaeomagnetic calibration process are modelled using a Bayesian hierarchical model. The archaeomagnetic data obtained from archaeological structures such as hearths, kilns or sets of bricks and tiles, exhibit considerable experimental errors and are typically more or less well dated by archaeological context, history or chronometric methods (14C, TL, dendrochronology, etc.). They can also be associated with stratigraphic observations which provide prior relative chronological information. The modelling we describe in this paper allows all these observations, on materials from a given period, to be linked together, and the use of penalized maximum likelihood for smoothing univariate, spherical or three-dimensional time series data allows representation of the secular variation of the geomagnetic field over time. The smooth curve we obtain (which takes the form of a penalized natural cubic spline) provides an adaptation to the effects of variability in the density of reference points over time. Since our model takes account of all the known errors in the archaeomagnetic calibration process, we are able to obtain a functional highest-posterior-density envelope on the new curve. With this new posterior estimate of the curve available to us, the Bayesian statistical framework then allows us to estimate the calendar dates of undated archaeological features (such as kilns) based on one, two or three geomagnetic parameters (inclination, declination and/or intensity). Date estimates are presented in much the same way as those that arise from radiocarbon dating. In order to illustrate the model and inference methods used, we will present results based on German archaeomagnetic data recently published by a German team.

Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

PubMed

Haem, Elham; Harling, Kajsa; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Karlsson, Mats O

2017-02-01

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1999-01-01

This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images.

PubMed

Wu, Allison Chia-Yi; Rifkin, Scott A

2015-03-27

Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle. We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments. The software classifies spots in these images well, with >95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification.

Stochastic goal-oriented error estimation with memory

NASA Astrophysics Data System (ADS)

Ackmann, Jan; Marotzke, Jochem; Korn, Peter

2017-11-01

We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.

Wind power error estimation in resource assessments.

PubMed

Rodríguez, Osvaldo; Del Río, Jesús A; Jaramillo, Oscar A; Martínez, Manuel

2015-01-01

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies.

Wind Power Error Estimation in Resource Assessments

PubMed Central

Rodríguez, Osvaldo; del Río, Jesús A.; Jaramillo, Oscar A.; Martínez, Manuel

2015-01-01

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies. PMID:26000444

Design Optimization for the Measurement Accuracy Improvement of a Large Range Nanopositioning Stage

PubMed Central

Torralba, Marta; Yagüe-Fabra, José Antonio; Albajez, José Antonio; Aguilar, Juan José

2016-01-01

Both an accurate machine design and an adequate metrology loop definition are critical factors when precision positioning represents a key issue for the final system performance. This article discusses the error budget methodology as an advantageous technique to improve the measurement accuracy of a 2D-long range stage during its design phase. The nanopositioning platform NanoPla is here presented. Its specifications, e.g., XY-travel range of 50 mm × 50 mm and sub-micrometric accuracy; and some novel designed solutions, e.g., a three-layer and two-stage architecture are described. Once defined the prototype, an error analysis is performed to propose improvement design features. Then, the metrology loop of the system is mathematically modelled to define the propagation of the different sources. Several simplifications and design hypothesis are justified and validated, including the assumption of rigid body behavior, which is demonstrated after a finite element analysis verification. The different error sources and their estimated contributions are enumerated in order to conclude with the final error values obtained from the error budget. The measurement deviations obtained demonstrate the important influence of the working environmental conditions, the flatness error of the plane mirror reflectors and the accurate manufacture and assembly of the components forming the metrological loop. Thus, a temperature control of ±0.1 °C results in an acceptable maximum positioning error for the developed NanoPla stage, i.e., 41 nm, 36 nm and 48 nm in X-, Y- and Z-axis, respectively. PMID:26761014

Dynamic Financial Constraints: Distinguishing Mechanism Design from Exogenously Incomplete Regimes*

PubMed Central

Karaivanov, Alexander; Townsend, Robert M.

2014-01-01

We formulate and solve a range of dynamic models of constrained credit/insurance that allow for moral hazard and limited commitment. We compare them to full insurance and exogenously incomplete financial regimes (autarky, saving only, borrowing and lending in a single asset). We develop computational methods based on mechanism design, linear programming, and maximum likelihood to estimate, compare, and statistically test these alternative dynamic models with financial/information constraints. Our methods can use both cross-sectional and panel data and allow for measurement error and unobserved heterogeneity. We estimate the models using data on Thai households running small businesses from two separate samples. We find that in the rural sample, the exogenously incomplete saving only and borrowing regimes provide the best fit using data on consumption, business assets, investment, and income. Family and other networks help consumption smoothing there, as in a moral hazard constrained regime. In contrast, in urban areas, we find mechanism design financial/information regimes that are decidedly less constrained, with the moral hazard model fitting best combined business and consumption data. We perform numerous robustness checks in both the Thai data and in Monte Carlo simulations and compare our maximum likelihood criterion with results from other metrics and data not used in the estimation. A prototypical counterfactual policy evaluation exercise using the estimation results is also featured. PMID:25246710

Decorrelation of the true and estimated classifier errors in high-dimensional settings.

PubMed

Hanczar, Blaise; Hua, Jianping; Dougherty, Edward R

2007-01-01

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, k-fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.

Error estimates for ice discharge calculated using the flux gate approach

NASA Astrophysics Data System (ADS)

Navarro, F. J.; Sánchez Gámez, P.

2017-12-01

Ice discharge to the ocean is usually estimated using the flux gate approach, in which ice flux is calculated through predefined flux gates close to the marine glacier front. However, published results usually lack a proper error estimate. In the flux calculation, both errors in cross-sectional area and errors in velocity are relevant. While for estimating the errors in velocity there are well-established procedures, the calculation of the error in the cross-sectional area requires the availability of ground penetrating radar (GPR) profiles transverse to the ice-flow direction. In this contribution, we use IceBridge operation GPR profiles collected in Ellesmere and Devon Islands, Nunavut, Canada, to compare the cross-sectional areas estimated using various approaches with the cross-sections estimated from GPR ice-thickness data. These error estimates are combined with those for ice-velocities calculated from Sentinel-1 SAR data, to get the error in ice discharge. Our preliminary results suggest, regarding area, that the parabolic cross-section approaches perform better than the quartic ones, which tend to overestimate the cross-sectional area for flight lines close to the central flowline. Furthermore, the results show that regional ice-discharge estimates made using parabolic approaches provide reasonable results, but estimates for individual glaciers can have large errors, up to 20% in cross-sectional area.

A Spectrally Selective Attenuation Mechanism-Based Kpar Algorithm for Biomass Heating Effect Simulation in the Open Ocean

NASA Astrophysics Data System (ADS)

Chen, Jun; Zhang, Xiangguang; Xing, Xiaogang; Ishizaka, Joji; Yu, Zhifeng

2017-12-01

Quantifying the diffuse attenuation coefficient of the photosynthetically available radiation (Kpar) can improve our knowledge of euphotic depth (Zeu) and biomass heating effects in the upper layers of oceans. An algorithm to semianalytically derive Kpar from remote sensing reflectance (Rrs) is developed for the global open oceans. This algorithm includes the following two portions: (1) a neural network model for deriving the diffuse attention coefficients (Kd) that considers the residual error in satellite Rrs, and (2) a three band depth-dependent Kpar algorithm (TDKA) for describing the spectrally selective attenuation mechanism of underwater solar radiation in the open oceans. This algorithm is evaluated with both in situ PAR profile data and satellite images, and the results show that it can produce acceptable PAR profile estimations while clearly removing the impacts of satellite residual errors on Kpar estimations. Furthermore, the performance of the TDKA algorithm is evaluated by its applicability in Zeu derivation and mean temperature within a mixed layer depth (TML) simulation, and the results show that it can significantly decrease the uncertainty in both compared with the classical chlorophyll-a concentration-based Kpar algorithm. Finally, the TDKA algorithm is applied in simulating biomass heating effects in the Sargasso Sea near Bermuda, with new Kpar data it is found that the biomass heating effects can lead to a 3.4°C maximum positive difference in temperature in the upper layers but could result in a 0.67°C maximum negative difference in temperature in the deep layers.

Finite Element A Posteriori Error Estimation for Heat Conduction. Degree awarded by George Washington Univ.

NASA Technical Reports Server (NTRS)

Lang, Christapher G.; Bey, Kim S. (Technical Monitor)

2002-01-01

This research investigates residual-based a posteriori error estimates for finite element approximations of heat conduction in single-layer and multi-layered materials. The finite element approximation, based upon hierarchical modelling combined with p-version finite elements, is described with specific application to a two-dimensional, steady state, heat-conduction problem. Element error indicators are determined by solving an element equation for the error with the element residual as a source, and a global error estimate in the energy norm is computed by collecting the element contributions. Numerical results of the performance of the error estimate are presented by comparisons to the actual error. Two methods are discussed and compared for approximating the element boundary flux. The equilibrated flux method provides more accurate results for estimating the error than the average flux method. The error estimation is applied to multi-layered materials with a modification to the equilibrated flux method to approximate the discontinuous flux along a boundary at the material interfaces. A directional error indicator is developed which distinguishes between the hierarchical modeling error and the finite element error. Numerical results are presented for single-layered materials which show that the directional indicators accurately determine which contribution to the total error dominates.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

PubMed

Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

2018-08-01

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.