Thermospheric mass density model error variance as a function of time scale

NASA Astrophysics Data System (ADS)

Emmert, J. T.; Sutton, E. K.

2017-12-01

In the increasingly crowded low-Earth orbit environment, accurate estimation of orbit prediction uncertainties is essential for collision avoidance. Poor characterization of such uncertainty can result in unnecessary and costly avoidance maneuvers (false positives) or disregard of a collision risk (false negatives). Atmospheric drag is a major source of orbit prediction uncertainty, and is particularly challenging to account for because it exerts a cumulative influence on orbital trajectories and is therefore not amenable to representation by a single uncertainty parameter. To address this challenge, we examine the variance of measured accelerometer-derived and orbit-derived mass densities with respect to predictions by thermospheric empirical models, using the data-minus-model variance as a proxy for model uncertainty. Our analysis focuses mainly on the power spectrum of the residuals, and we construct an empirical model of the variance as a function of time scale (from 1 hour to 10 years), altitude, and solar activity. We find that the power spectral density approximately follows a power-law process but with an enhancement near the 27-day solar rotation period. The residual variance increases monotonically with altitude between 250 and 550 km. There are two components to the variance dependence on solar activity: one component is 180 degrees out of phase (largest variance at solar minimum), and the other component lags 2 years behind solar maximum (largest variance in the descending phase of the solar cycle).

AQMEII3 evaluation of regional NA/EU simulations and ...

EPA Pesticide Factsheets

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impac

Optimized two-frequency phase-measuring-profilometry light-sensor temporal-noise sensitivity.

PubMed

Li, Jielin; Hassebrook, Laurence G; Guan, Chun

2003-01-01

Temporal frame-to-frame noise in multipattern structured light projection can significantly corrupt depth measurement repeatability. We present a rigorous stochastic analysis of phase-measuring-profilometry temporal noise as a function of the pattern parameters and the reconstruction coefficients. The analysis is used to optimize the two-frequency phase measurement technique. In phase-measuring profilometry, a sequence of phase-shifted sine-wave patterns is projected onto a surface. In two-frequency phase measurement, two sets of pattern sequences are used. The first, low-frequency set establishes a nonambiguous depth estimate, and the second, high-frequency set is unwrapped, based on the low-frequency estimate, to obtain an accurate depth estimate. If the second frequency is too low, then depth error is caused directly by temporal noise in the phase measurement. If the second frequency is too high, temporal noise triggers ambiguous unwrapping, resulting in depth measurement error. We present a solution for finding the second frequency, where intensity noise variance is at its minimum.

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

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

Matsuo, Yukinori, E-mail: ymatsuo@kuhp.kyoto-u.ac.

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiplemore » causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.« less

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

PubMed

Holmes, John B; Dodds, Ken G; Lee, Michael A

2017-03-02

An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

Modeling Multiplicative Error Variance: An Example Predicting Tree Diameter from Stump Dimensions in Baldcypress

Treesearch

Bernard R. Parresol

1993-01-01

In the context of forest modeling, it is often reasonable to assume a multiplicative heteroscedastic error structure to the data. Under such circumstances ordinary least squares no longer provides minimum variance estimates of the model parameters. Through study of the error structure, a suitable error variance model can be specified and its parameters estimated. This...

On the performance of digital phase locked loops in the threshold region

NASA Technical Reports Server (NTRS)

Hurst, G. T.; Gupta, S. C.

1974-01-01

Extended Kalman filter algorithms are used to obtain a digital phase lock loop structure for demodulation of angle modulated signals. It is shown that the error variance equations obtained directly from this structure enable one to predict threshold if one retains higher frequency terms. This is in sharp contrast to the similar analysis of the analog phase lock loop, where the higher frequency terms are filtered out because of the low pass filter in the loop. Results are compared to actual simulation results and threshold region results obtained previously.

Timing performance of phased-locked loops in optical pulse position modulation communication systems

NASA Technical Reports Server (NTRS)

Lafaw, D. A.; Gardner, C. S.

1984-01-01

An optical digital communication system requires that an accurate clock signal be available at the receiver for proper synchronization with the transmitted signal. Phase synchronization is especially critical in M-ary pulse position modulation (PPM) systems where the optimum decision scheme is an energy detector which compares the energy in each of M time slots to decide which of M possible words was sent. Timing errors cause energy spillover into adjacent time slots (a form of intersymbol interference) so that only a portion of the signal energy may be attributed to the correct time slot. This effect decreases the effective signal, increases the effective noise, and increases the probability of error. A timing subsystem for a satellite-to-satellite optical PPM communication link is simulated. The receiver employs direct photodetection, preprocessing of the detected signal, and a phase-locked loop for timing synchronization. The variance of the relative phase error is examined under varying signal strength conditions as an indication of loop performance, and simulation results are compared to theoretical calculations.

Timing performance of phased-locked loops in optical pulse position modulation communication systems

NASA Astrophysics Data System (ADS)

Lafaw, D. A.; Gardner, C. S.

1984-08-01

An optical digital communication system requires that an accurate clock signal be available at the receiver for proper synchronization with the transmitted signal. Phase synchronization is especially critical in M-ary pulse position modulation (PPM) systems where the optimum decision scheme is an energy detector which compares the energy in each of M time slots to decide which of M possible words was sent. Timing errors cause energy spillover into adjacent time slots (a form of intersymbol interference) so that only a portion of the signal energy may be attributed to the correct time slot. This effect decreases the effective signal, increases the effective noise, and increases the probability of error. A timing subsystem for a satellite-to-satellite optical PPM communication link is simulated. The receiver employs direct photodetection, preprocessing of the detected signal, and a phase-locked loop for timing synchronization. The variance of the relative phase error is examined under varying signal strength conditions as an indication of loop performance, and simulation results are compared to theoretical calculations.

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

NASA Astrophysics Data System (ADS)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

Stratospheric Assimilation of Chemical Tracer Observations Using a Kalman Filter. Pt. 2; Chi-Square Validated Results and Analysis of Variance and Correlation Dynamics

NASA Technical Reports Server (NTRS)

Menard, Richard; Chang, Lang-Ping

1998-01-01

A Kalman filter system designed for the assimilation of limb-sounding observations of stratospheric chemical tracers, which has four tunable covariance parameters, was developed in Part I (Menard et al. 1998) The assimilation results of CH4 observations from the Cryogenic Limb Array Etalon Sounder instrument (CLAES) and the Halogen Observation Experiment instrument (HALOE) on board of the Upper Atmosphere Research Satellite are described in this paper. A robust (chi)(sup 2) criterion, which provides a statistical validation of the forecast and observational error covariances, was used to estimate the tunable variance parameters of the system. In particular, an estimate of the model error variance was obtained. The effect of model error on the forecast error variance became critical after only three days of assimilation of CLAES observations, although it took 14 days of forecast to double the initial error variance. We further found that the model error due to numerical discretization as arising in the standard Kalman filter algorithm, is comparable in size to the physical model error due to wind and transport modeling errors together. Separate assimilations of CLAES and HALOE observations were compared to validate the state estimate away from the observed locations. A wave-breaking event that took place several thousands of kilometers away from the HALOE observation locations was well captured by the Kalman filter due to highly anisotropic forecast error correlations. The forecast error correlation in the assimilation of the CLAES observations was found to have a structure similar to that in pure forecast mode except for smaller length scales. Finally, we have conducted an analysis of the variance and correlation dynamics to determine their relative importance in chemical tracer assimilation problems. Results show that the optimality of a tracer assimilation system depends, for the most part, on having flow-dependent error correlation rather than on evolving the error variance.

AQMEII3: the EU and NA regional scale program of the ...

EPA Pesticide Factsheets

The presentation builds on the work presented last year at the 14th CMAS meeting and it is applied to the work performed in the context of the AQMEII-HTAP collaboration. The analysis is conducted within the framework of the third phase of AQMEII (Air Quality Model Evaluation International Initiative) and encompasses the gauging of model performance through measurement-to-model comparison, error decomposition and time series analysis of the models biases. Through the comparison of several regional-scale chemistry transport modelling systems applied to simulate meteorology and air quality over two continental areas, this study aims at i) apportioning the error to the responsible processes through time-scale analysis, and ii) help detecting causes of models error, and iii) identify the processes and scales most urgently requiring dedicated investigations. The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while the apportioning of the error into its constituent parts (bias, variance and covariance) can help assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the previous phases of AQMEII. The National Exposure Research Laboratory (NERL) Computational Exposur

Prediction-error variance in Bayesian model updating: a comparative study

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

Timing performance of phase-locked loops in optical pulse position modulation communication systems

NASA Astrophysics Data System (ADS)

Lafaw, D. A.

In an optical digital communication system, an accurate clock signal must be available at the receiver to provide proper synchronization with the transmitted signal. Phase synchronization is especially critical in M-ary pulse position modulation (PPM) systems where the optimum decision scheme is an energy detector which compares the energy in each of M time slots to decide which of M possible words was sent. A timing error causes energy spillover into adjacent time slots (a form of intersymbol interference) so that only a portion of the signal energy may be attributed to the correct time slot. This effect decreases the effective signal, increases the effective noise, and increases the probability of error. This report simulates a timing subsystem for a satellite-to-satellite optical PPM communication link. The receiver employs direct photodetection, preprocessing of the optical signal, and a phase-locked loop for timing synchronization. The photodetector output is modeled as a filtered, doubly stochastic Poisson shot noise process. The variance of the relative phase error is examined under varying signal strength conditions as an indication of loop performance, and simulation results are compared to theoretical relations.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

PubMed

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-11-18

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration-which are the basis of tracking error estimation-are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (-0.25 cycle, 0.25 cycle) to (-0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers

PubMed Central

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-01-01

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz. PMID:29156581

Movement trajectory smoothness is not associated with the endpoint accuracy of rapid multi-joint arm movements in young and older adults

PubMed Central

Poston, Brach; Van Gemmert, Arend W.A.; Sharma, Siddharth; Chakrabarti, Somesh; Zavaremi, Shahrzad H.; Stelmach, George

2013-01-01

The minimum variance theory proposes that motor commands are corrupted by signal-dependent noise and smooth trajectories with low noise levels are selected to minimize endpoint error and endpoint variability. The purpose of the study was to determine the contribution of trajectory smoothness to the endpoint accuracy and endpoint variability of rapid multi-joint arm movements. Young and older adults performed arm movements (4 blocks of 25 trials) as fast and as accurately as possible to a target with the right (dominant) arm. Endpoint accuracy and endpoint variability along with trajectory smoothness and error were quantified for each block of trials. Endpoint error and endpoint variance were greater in older adults compared with young adults, but decreased at a similar rate with practice for the two age groups. The greater endpoint error and endpoint variance exhibited by older adults were primarily due to impairments in movement extent control and not movement direction control. The normalized jerk was similar for the two age groups, but was not strongly associated with endpoint error or endpoint variance for either group. However, endpoint variance was strongly associated with endpoint error for both the young and older adults. Finally, trajectory error was similar for both groups and was weakly associated with endpoint error for the older adults. The findings are not consistent with the predictions of the minimum variance theory, but support and extend previous observations that movement trajectories and endpoints are planned independently. PMID:23584101

Parameter dimension of turbulence-induced phase errors and its effects on estimation in phase diversity

NASA Technical Reports Server (NTRS)

Thelen, Brian J.; Paxman, Richard G.

1994-01-01

The method of phase diversity has been used in the context of incoherent imaging to estimate jointly an object that is being imaged and phase aberrations induced by atmospheric turbulence. The method requires a parametric model for the phase-aberration function. Typically, the parameters are coefficients to a finite set of basis functions. Care must be taken in selecting a parameterization that properly balances accuracy in the representation of the phase-aberration function with stability in the estimates. It is well known that over parameterization can result in unstable estimates. Thus a certain amount of model mismatch is often desirable. We derive expressions that quantify the bias and variance in object and aberration estimates as a function of parameter dimension.

Comment on Hoffman and Rovine (2007): SPSS MIXED can estimate models with heterogeneous variances.

PubMed

Weaver, Bruce; Black, Ryan A

2015-06-01

Hoffman and Rovine (Behavior Research Methods, 39:101-117, 2007) have provided a very nice overview of how multilevel models can be useful to experimental psychologists. They included two illustrative examples and provided both SAS and SPSS commands for estimating the models they reported. However, upon examining the SPSS syntax for the models reported in their Table 3, we found no syntax for models 2B and 3B, both of which have heterogeneous error variances. Instead, there is syntax that estimates similar models with homogeneous error variances and a comment stating that SPSS does not allow heterogeneous errors. But that is not correct. We provide SPSS MIXED commands to estimate models 2B and 3B with heterogeneous error variances and obtain results nearly identical to those reported by Hoffman and Rovine in their Table 3. Therefore, contrary to the comment in Hoffman and Rovine's syntax file, SPSS MIXED can estimate models with heterogeneous error variances.

Ensemble codes involving hippocampal neurons are at risk during delayed performance tests.

PubMed

Hampson, R E; Deadwyler, S A

1996-11-26

Multielectrode recording techniques were used to record ensemble activity from 10 to 16 simultaneously active CA1 and CA3 neurons in the rat hippocampus during performance of a spatial delayed-nonmatch-to-sample task. Extracted sources of variance were used to assess the nature of two different types of errors that accounted for 30% of total trials. The two types of errors included ensemble "miscodes" of sample phase information and errors associated with delay-dependent corruption or disappearance of sample information at the time of the nonmatch response. Statistical assessment of trial sequences and associated "strength" of hippocampal ensemble codes revealed that miscoded error trials always followed delay-dependent error trials in which encoding was "weak," indicating that the two types of errors were "linked." It was determined that the occurrence of weakly encoded, delay-dependent error trials initiated an ensemble encoding "strategy" that increased the chances of being correct on the next trial and avoided the occurrence of further delay-dependent errors. Unexpectedly, the strategy involved "strongly" encoding response position information from the prior (delay-dependent) error trial and carrying it forward to the sample phase of the next trial. This produced a miscode type error on trials in which the "carried over" information obliterated encoding of the sample phase response on the next trial. Application of this strategy, irrespective of outcome, was sufficient to reorient the animal to the proper between trial sequence of response contingencies (nonmatch-to-sample) and boost performance to 73% correct on subsequent trials. The capacity for ensemble analyses of strength of information encoding combined with statistical assessment of trial sequences therefore provided unique insight into the "dynamic" nature of the role hippocampus plays in delay type memory tasks.

Replica approach to mean-variance portfolio optimization

NASA Astrophysics Data System (ADS)

Varga-Haszonits, Istvan; Caccioli, Fabio; Kondor, Imre

2016-12-01

We consider the problem of mean-variance portfolio optimization for a generic covariance matrix subject to the budget constraint and the constraint for the expected return, with the application of the replica method borrowed from the statistical physics of disordered systems. We find that the replica symmetry of the solution does not need to be assumed, but emerges as the unique solution of the optimization problem. We also check the stability of this solution and find that the eigenvalues of the Hessian are positive for r  =  N/T  <  1, where N is the dimension of the portfolio and T the length of the time series used to estimate the covariance matrix. At the critical point r  =  1 a phase transition is taking place. The out of sample estimation error blows up at this point as 1/(1  -  r), independently of the covariance matrix or the expected return, displaying the universality not only of the critical exponent, but also the critical point. As a conspicuous illustration of the dangers of in-sample estimates, the optimal in-sample variance is found to vanish at the critical point inversely proportional to the divergent estimation error.

Frequency noise measurement of diode-pumped Nd:YAG ring lasers

NASA Technical Reports Server (NTRS)

Chen, Chien-Chung; Win, Moe Zaw

1990-01-01

The combined frequency noise spectrum of two model 120-01A nonplanar ring oscillator lasers was measured by first heterodyne detecting the IF signal and then measuring the IF frequency noise using an RF frequency discriminator. The results indicated the presence of a 1/f-squared noise component in the power-spectral density of the frequency fluctuations between 1 Hz and 1 kHz. After incorporating this 1/f-squared into the analysis of the optical phase tracking loop, the measured phase error variance closely matches the theoretical predictions.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

Age-related variation in genetic control of height growth in Douglas-fir.

PubMed

Namkoong, G; Usanis, R A; Silen, R R

1972-01-01

The development of genetic variances in height growth of Douglas-fir over a 53-year period is analyzed and found to fall into three periods. In the juvenile period, variances in environmental error increase logarithmically, genetic variance within populations exists at moderate levels, and variance among populations is low but increasing. In the early reproductive period, the response to environmental sources of error variance is restricted, genetic variance within populations disappears, and populational differences strongly emerge but do not increase as expected. In the later period, environmental error again increases rapidly, but genetic variance within populations does not reappear and population differences are maintained at about the same level as established in the early reproductive period. The change between the juvenile and early reproductive periods is perhaps associated with the onset of ecological dominance and significant allocations of energy to reproduction.

A simulation study of Large Area Crop Inventory Experiment (LACIE) technology

NASA Technical Reports Server (NTRS)

Ziegler, L. (Principal Investigator); Potter, J.

1979-01-01

The author has identified the following significant results. The LACIE performance predictor (LPP) was used to replicate LACIE phase 2 for a 15 year period, using accuracy assessment results for phase 2 error components. Results indicated that the (LPP) simulated the LACIE phase 2 procedures reasonably well. For the 15 year simulation, only 7 of the 15 production estimates were within 10 percent of the true production. The simulations indicated that the acreage estimator, based on CAMS phase 2 procedures, has a negative bias. This bias was too large to support the 90/90 criterion with the CV observed and simulated for the phase 2 production estimator. Results of this simulation study validate the theory that the acreage variance estimator in LACIE was conservative.

Evaluation of three lidar scanning strategies for turbulence measurements

NASA Astrophysics Data System (ADS)

Newman, J. F.; Klein, P. M.; Wharton, S.; Sathe, A.; Bonin, T. A.; Chilson, P. B.; Muschinski, A.

2015-11-01

Several errors occur when a traditional Doppler-beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers. Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

Evaluation of three lidar scanning strategies for turbulence measurements

NASA Astrophysics Data System (ADS)

Newman, Jennifer F.; Klein, Petra M.; Wharton, Sonia; Sathe, Ameya; Bonin, Timothy A.; Chilson, Phillip B.; Muschinski, Andreas

2016-05-01

Several errors occur when a traditional Doppler beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

Phase Noise Influence in Long-range Coherent Optical OFDM Systems with Delay Detection, IFFT Multiplexing and FFT Demodulation

NASA Astrophysics Data System (ADS)

Jacobsen, Gunnar; Xu, Tianhua; Popov, Sergei; Sergeyev, Sergey; Zhang, Yimo

2012-12-01

We present a study of the influence of dispersion induced phase noise for CO-OFDM systems using FFT multiplexing/IFFT demultiplexing techniques (software based). The software based system provides a method for a rigorous evaluation of the phase noise variance caused by Common Phase Error (CPE) and Inter-Carrier Interference (ICI) including - for the first time to our knowledge - in explicit form the effect of equalization enhanced phase noise (EEPN). This, in turns, leads to an analytic BER specification. Numerical results focus on a CO-OFDM system with 10-25 GS/s QPSK channel modulation. A worst case constellation configuration is identified for the phase noise influence and the resulting BER is compared to the BER of a conventional single channel QPSK system with the same capacity as the CO-OFDM implementation. Results are evaluated as a function of transmission distance. For both types of systems, the phase noise variance increases significantly with increasing transmission distance. For a total capacity of 400 (1000) Gbit/s, the transmission distance to have the BER < 10-2 for the worst case CO-OFDM design is less than 800 and 460 km, respectively, whereas for a single channel QPSK system it is less than 1400 and 560 km.

Adjustment of Measurements with Multiplicative Errors: Error Analysis, Estimates of the Variance of Unit Weight, and Effect on Volume Estimation from LiDAR-Type Digital Elevation Models

PubMed Central

Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan

2014-01-01

Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM. PMID:24434880

Measurement System Characterization in the Presence of Measurement Errors

NASA Technical Reports Server (NTRS)

Commo, Sean A.

2012-01-01

In the calibration of a measurement system, data are collected in order to estimate a mathematical model between one or more factors of interest and a response. Ordinary least squares is a method employed to estimate the regression coefficients in the model. The method assumes that the factors are known without error; yet, it is implicitly known that the factors contain some uncertainty. In the literature, this uncertainty is known as measurement error. The measurement error affects both the estimates of the model coefficients and the prediction, or residual, errors. There are some methods, such as orthogonal least squares, that are employed in situations where measurement errors exist, but these methods do not directly incorporate the magnitude of the measurement errors. This research proposes a new method, known as modified least squares, that combines the principles of least squares with knowledge about the measurement errors. This knowledge is expressed in terms of the variance ratio - the ratio of response error variance to measurement error variance.

Smooth empirical Bayes estimation of observation error variances in linear systems

NASA Technical Reports Server (NTRS)

Martz, H. F., Jr.; Lian, M. W.

1972-01-01

A smooth empirical Bayes estimator was developed for estimating the unknown random scale component of each of a set of observation error variances. It is shown that the estimator possesses a smaller average squared error loss than other estimators for a discrete time linear system.

Evaluation of three lidar scanning strategies for turbulence measurements

DOE PAGES

Newman, Jennifer F.; Klein, Petra M.; Wharton, Sonia; ...

2016-05-03

Several errors occur when a traditional Doppler beam swinging (DBS) or velocity–azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused bymore » VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.« less

Evaluation of three lidar scanning strategies for turbulence measurements

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

Newman, Jennifer F.; Klein, Petra M.; Wharton, Sonia

Several errors occur when a traditional Doppler beam swinging (DBS) or velocity–azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused bymore » VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.« less

A comparison of methods for DPLL loop filter design

NASA Technical Reports Server (NTRS)

Aguirre, S.; Hurd, W. J.; Kumar, R.; Statman, J.

1986-01-01

Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.

Generalized Variance Function Applications in Forestry

Treesearch

James Alegria; Charles T. Scott; Charles T. Scott

1991-01-01

Adequately predicting the sampling errors of tabular data can reduce printing costs by eliminating the need to publish separate sampling error tables. Two generalized variance functions (GVFs) found in the literature and three GVFs derived for this study were evaluated for their ability to predict the sampling error of tabular forestry estimates. The recommended GVFs...

A Practical Methodology for Quantifying Random and Systematic Components of Unexplained Variance in a Wind Tunnel

NASA Technical Reports Server (NTRS)

Deloach, Richard; Obara, Clifford J.; Goodman, Wesley L.

2012-01-01

This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.

Smoothed Spectra, Ogives, and Error Estimates for Atmospheric Turbulence Data

NASA Astrophysics Data System (ADS)

Dias, Nelson Luís

2018-01-01

A systematic evaluation is conducted of the smoothed spectrum, which is a spectral estimate obtained by averaging over a window of contiguous frequencies. The technique is extended to the ogive, as well as to the cross-spectrum. It is shown that, combined with existing variance estimates for the periodogram, the variance—and therefore the random error—associated with these estimates can be calculated in a straightforward way. The smoothed spectra and ogives are biased estimates; with simple power-law analytical models, correction procedures are devised, as well as a global constraint that enforces Parseval's identity. Several new results are thus obtained: (1) The analytical variance estimates compare well with the sample variance calculated for the Bartlett spectrum and the variance of the inertial subrange of the cospectrum is shown to be relatively much larger than that of the spectrum. (2) Ogives and spectra estimates with reduced bias are calculated. (3) The bias of the smoothed spectrum and ogive is shown to be negligible at the higher frequencies. (4) The ogives and spectra thus calculated have better frequency resolution than the Bartlett spectrum, with (5) gradually increasing variance and relative error towards the low frequencies. (6) Power-law identification and extraction of the rate of dissipation of turbulence kinetic energy are possible directly from the ogive. (7) The smoothed cross-spectrum is a valid inner product and therefore an acceptable candidate for coherence and spectral correlation coefficient estimation by means of the Cauchy-Schwarz inequality. The quadrature, phase function, coherence function and spectral correlation function obtained from the smoothed spectral estimates compare well with the classical ones derived from the Bartlett spectrum.

Evaluation of TRMM Ground-Validation Radar-Rain Errors Using Rain Gauge Measurements

NASA Technical Reports Server (NTRS)

Wang, Jianxin; Wolff, David B.

2009-01-01

Ground-validation (GV) radar-rain products are often utilized for validation of the Tropical Rainfall Measuring Mission (TRMM) spaced-based rain estimates, and hence, quantitative evaluation of the GV radar-rain product error characteristics is vital. This study uses quality-controlled gauge data to compare with TRMM GV radar rain rates in an effort to provide such error characteristics. The results show that significant differences of concurrent radar-gauge rain rates exist at various time scales ranging from 5 min to 1 day, despite lower overall long-term bias. However, the differences between the radar area-averaged rain rates and gauge point rain rates cannot be explained as due to radar error only. The error variance separation method is adapted to partition the variance of radar-gauge differences into the gauge area-point error variance and radar rain estimation error variance. The results provide relatively reliable quantitative uncertainty evaluation of TRMM GV radar rain estimates at various times scales, and are helpful to better understand the differences between measured radar and gauge rain rates. It is envisaged that this study will contribute to better utilization of GV radar rain products to validate versatile spaced-based rain estimates from TRMM, as well as the proposed Global Precipitation Measurement, and other satellites.

Adaptation to Laterally Displacing Prisms in Anisometropic Amblyopia.

PubMed

Sklar, Jaime C; Goltz, Herbert C; Gane, Luke; Wong, Agnes M F

2015-06-01

Using visual feedback to modify sensorimotor output in response to changes in the external environment is essential for daily function. Prism adaptation is a well-established experimental paradigm to quantify sensorimotor adaptation; that is, how the sensorimotor system adapts to an optically-altered visuospatial environment. Amblyopia is a neurodevelopmental disorder characterized by spatiotemporal deficits in vision that impacts manual and oculomotor function. This study explored the effects of anisometropic amblyopia on prism adaptation. Eight participants with anisometropic amblyopia and 11 visually-normal adults, all right-handed, were tested. Participants pointed to visual targets and were presented with feedback of hand position near the terminus of limb movement in three blocks: baseline, adaptation, and deadaptation. Adaptation was induced by viewing with binocular 11.4° (20 prism diopter [PD]) left-shifting prisms. All tasks were performed during binocular viewing. Participants with anisometropic amblyopia required significantly more trials (i.e., increased time constant) to adapt to prismatic optical displacement than visually-normal controls. During the rapid error correction phase of adaptation, people with anisometropic amblyopia also exhibited greater variance in motor output than visually-normal controls. Amblyopia impacts on the ability to adapt the sensorimotor system to an optically-displaced visual environment. The increased time constant and greater variance in motor output during the rapid error correction phase of adaptation may indicate deficits in processing of visual information as a result of degraded spatiotemporal vision in amblyopia.

Estimation of error on the cross-correlation, phase and time lag between evenly sampled light curves

NASA Astrophysics Data System (ADS)

Misra, R.; Bora, A.; Dewangan, G.

2018-04-01

Temporal analysis of radiation from Astrophysical sources like Active Galactic Nuclei, X-ray Binaries and Gamma-ray bursts provides information on the geometry and sizes of the emitting regions. Establishing that two light-curves in different energy bands are correlated, and measuring the phase and time-lag between them is an important and frequently used temporal diagnostic. Generally the estimates are done by dividing the light-curves into large number of adjacent intervals to find the variance or by using numerically expensive simulations. In this work we have presented alternative expressions for estimate of the errors on the cross-correlation, phase and time-lag between two shorter light-curves when they cannot be divided into segments. Thus the estimates presented here allow for analysis of light-curves with relatively small number of points, as well as to obtain information on the longest time-scales available. The expressions have been tested using 200 light curves simulated from both white and 1 / f stochastic processes with measurement errors. We also present an application to the XMM-Newton light-curves of the Active Galactic Nucleus, Akn 564. The example shows that the estimates presented here allow for analysis of light-curves with relatively small (∼ 1000) number of points.

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data

NASA Astrophysics Data System (ADS)

Correia, Carlos M.; Bond, Charlotte Z.; Sauvage, Jean-François; Fusco, Thierry; Conan, Rodolphe; Wizinowich, Peter L.

2017-10-01

We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors whilst minimizing the overall residual variance. We discuss applications to (i) analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii) post-coronagraphic contrast enhancement, (iii) filter optimization for real-time wavefront reconstruction, and (iv) PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that $\\sim$60 nm rms error reduction can be achieved with the distributed Kalman filter embodying anti- aliasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few ${\\lambda}/D$ separations ($\\sim1-5{\\lambda}/D$) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.

Toward Joint Hypothesis-Tests Seismic Event Screening Analysis: Ms|mb and Event Depth

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

Anderson, Dale; Selby, Neil

2012-08-14

Well established theory can be used to combine single-phenomenology hypothesis tests into a multi-phenomenology event screening hypothesis test (Fisher's and Tippett's tests). Commonly used standard error in Ms:mb event screening hypothesis test is not fully consistent with physical basis. Improved standard error - Better agreement with physical basis, and correctly partitions error to include Model Error as a component of variance, correctly reduces station noise variance through network averaging. For 2009 DPRK test - Commonly used standard error 'rejects' H0 even with better scaling slope ({beta} = 1, Selby et al.), improved standard error 'fails to rejects' H0.

Within-Tunnel Variations in Pressure Data for Three Transonic Wind Tunnels

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2014-01-01

This paper compares the results of pressure measurements made on the same test article with the same test matrix in three transonic wind tunnels. A comparison is presented of the unexplained variance associated with polar replicates acquired in each tunnel. The impact of a significance component of systematic (not random) unexplained variance is reviewed, and the results of analyses of variance are presented to assess the degree of significant systematic error in these representative wind tunnel tests. Total uncertainty estimates are reported for 140 samples of pressure data, quantifying the effects of within-polar random errors and between-polar systematic bias errors.

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.

Psycho-Motor and Error Enabled Simulations: Modeling Vulnerable Skills in the Pre Mastery Phase - Medical Practice Initiative Procedural Skill Decay and Maintenance (MPI-PSD)

DTIC Science & Technology

2016-12-01

repair, bowel anastomosis, central venous catheterization , and bladder catheterization . We performed a multivariate analysis of variance (MANOVA) to...DC, Gould MK. Preventing complications of central venous catheterization . N Engl J Med 2003;348:1123–33. 15. Maithel S, Sierra R, Korndorffer J, et...subclavian-vein catheterization . N Engl J Med. 1994;331(26):1735-1738. 2. Taylor RW, Palagiri AV. Central venous catheter- ization. Crit Care Med. 2007;35(5

Precision, Reliability, and Effect Size of Slope Variance in Latent Growth Curve Models: Implications for Statistical Power Analysis

PubMed Central

Brandmaier, Andreas M.; von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman; Hertzog, Christopher

2018-01-01

Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse of effective error. Effective error is determined by instrument reliability and the temporal arrangement of measurement occasions. However, it also depends on another central LGCM component, the variance of the latent intercept and its covariance with the latent slope. We derive a new reliability index for LGCM slope variance—effective curve reliability (ECR)—by scaling slope variance against effective error. ECR is interpretable as a standardized effect size index. We demonstrate how effective error, ECR, and statistical power for a likelihood ratio test of zero slope variance formally relate to each other and how they function as indices of statistical power. We also provide a computational approach to derive ECR for arbitrary intercept-slope covariance. With practical use cases, we argue for the complementary utility of the proposed indices of a study's sensitivity to detect slope variance when making a priori longitudinal design decisions or communicating study designs. PMID:29755377

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Station Correction Uncertainty in Multiple Event Location Algorithms and the Effect on Error Ellipses

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

Erickson, Jason P.; Carlson, Deborah K.; Ortiz, Anne

Accurate location of seismic events is crucial for nuclear explosion monitoring. There are several sources of error in seismic location that must be taken into account to obtain high confidence results. Most location techniques account for uncertainties in the phase arrival times (measurement error) and the bias of the velocity model (model error), but they do not account for the uncertainty of the velocity model bias. By determining and incorporating this uncertainty in the location algorithm we seek to improve the accuracy of the calculated locations and uncertainty ellipses. In order to correct for deficiencies in the velocity model, itmore » is necessary to apply station specific corrections to the predicted arrival times. Both master event and multiple event location techniques assume that the station corrections are known perfectly, when in reality there is an uncertainty associated with these corrections. For multiple event location algorithms that calculate station corrections as part of the inversion, it is possible to determine the variance of the corrections. The variance can then be used to weight the arrivals associated with each station, thereby giving more influence to stations with consistent corrections. We have modified an existing multiple event location program (based on PMEL, Pavlis and Booker, 1983). We are exploring weighting arrivals with the inverse of the station correction standard deviation as well using the conditional probability of the calculated station corrections. This is in addition to the weighting already given to the measurement and modeling error terms. We re-locate a group of mining explosions that occurred at Black Thunder, Wyoming, and compare the results to those generated without accounting for station correction uncertainty.« less

Reliability of a Longitudinal Sequence of Scale Ratings

ERIC Educational Resources Information Center

Laenen, Annouschka; Alonso, Ariel; Molenberghs, Geert; Vangeneugden, Tony

2009-01-01

Reliability captures the influence of error on a measurement and, in the classical setting, is defined as one minus the ratio of the error variance to the total variance. Laenen, Alonso, and Molenberghs ("Psychometrika" 73:443-448, 2007) proposed an axiomatic definition of reliability and introduced the R[subscript T] coefficient, a measure of…

Impact of Measurement Error on Statistical Power: Review of an Old Paradox.

ERIC Educational Resources Information Center

Williams, Richard H.; And Others

1995-01-01

The paradox that a Student t-test based on pretest-posttest differences can attain its greatest power when the difference score reliability is zero was explained by demonstrating that power is not a mathematical function of reliability unless either true score variance or error score variance is constant. (SLD)

High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

PubMed Central

Daye, Z. John; Chen, Jinbo; Li, Hongzhe

2011-01-01

Summary We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis. PMID:22547833

Identification of 'Point A' as the prevalent source of error in cephalometric analysis of lateral radiographs.

PubMed

Grogger, P; Sacher, C; Weber, S; Millesi, G; Seemann, R

2018-04-10

Deviations in measuring dentofacial components in a lateral X-ray represent a major hurdle in the subsequent treatment of dysgnathic patients. In a retrospective study, we investigated the most prevalent source of error in the following commonly used cephalometric measurements: the angles Sella-Nasion-Point A (SNA), Sella-Nasion-Point B (SNB) and Point A-Nasion-Point B (ANB); the Wits appraisal; the anteroposterior dysplasia indicator (APDI); and the overbite depth indicator (ODI). Preoperative lateral radiographic images of patients with dentofacial deformities were collected and the landmarks digitally traced by three independent raters. Cephalometric analysis was automatically performed based on 1116 tracings. Error analysis identified the x-coordinate of Point A as the prevalent source of error in all investigated measurements, except SNB, in which it is not incorporated. In SNB, the y-coordinate of Nasion predominated error variance. SNB showed lowest inter-rater variation. In addition, our observations confirmed previous studies showing that landmark identification variance follows characteristic error envelopes in the highest number of tracings analysed up to now. Variance orthogonal to defining planes was of relevance, while variance parallel to planes was not. Taking these findings into account, orthognathic surgeons as well as orthodontists would be able to perform cephalometry more accurately and accomplish better therapeutic results. Copyright © 2018 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)

PubMed Central

Xu, Chonggang; Gertner, George

2013-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements. PMID:24143037

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST).

PubMed

Xu, Chonggang; Gertner, George

2011-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements.

Specificity of reliable change models and review of the within-subjects standard deviation as an error term.

PubMed

Hinton-Bayre, Anton D

2011-02-01

There is an ongoing debate over the preferred method(s) for determining the reliable change (RC) in individual scores over time. In the present paper, specificity comparisons of several classic and contemporary RC models were made using a real data set. This included a more detailed review of a new RC model recently proposed in this journal, that used the within-subjects standard deviation (WSD) as the error term. It was suggested that the RC(WSD) was more sensitive to change and theoretically superior. The current paper demonstrated that even in the presence of mean practice effects, false-positive rates were comparable across models when reliability was good and initial and retest variances were equivalent. However, when variances differed, discrepancies in classification across models became evident. Notably, the RC using the WSD provided unacceptably high false-positive rates in this setting. It was considered that the WSD was never intended for measuring change in this manner. The WSD actually combines systematic and error variance. The systematic variance comes from measurable between-treatment differences, commonly referred to as practice effect. It was further demonstrated that removal of the systematic variance and appropriate modification of the residual error term for the purpose of testing individual change yielded an error term already published and criticized in the literature. A consensus on the RC approach is needed. To that end, further comparison of models under varied conditions is encouraged.

Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance

PubMed Central

Eaton, Jeffrey W.; Bao, Le

2017-01-01

Objectives The aim of the study was to propose and demonstrate an approach to allow additional nonsampling uncertainty about HIV prevalence measured at antenatal clinic sentinel surveillance (ANC-SS) in model-based inferences about trends in HIV incidence and prevalence. Design Mathematical model fitted to surveillance data with Bayesian inference. Methods We introduce a variance inflation parameter σinfl2 that accounts for the uncertainty of nonsampling errors in ANC-SS prevalence. It is additive to the sampling error variance. Three approaches are tested for estimating σinfl2 using ANC-SS and household survey data from 40 subnational regions in nine countries in sub-Saharan, as defined in UNAIDS 2016 estimates. Methods were compared using in-sample fit and out-of-sample prediction of ANC-SS data, fit to household survey prevalence data, and the computational implications. Results Introducing the additional variance parameter σinfl2 increased the error variance around ANC-SS prevalence observations by a median of 2.7 times (interquartile range 1.9–3.8). Using only sampling error in ANC-SS prevalence ( σinfl2=0), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter σinfl2. The revised probabilistic model improved model fit to household survey prevalence and increased epidemic uncertainty intervals most during the early epidemic period before 2005. Estimating σinfl2 did not increase the computational cost of model fitting. Conclusions: We recommend estimating nonsampling error in ANC-SS as an additional parameter in Bayesian inference using the Estimation and Projection Package model. This approach may prove useful for incorporating other data sources such as routine prevalence from Prevention of mother-to-child transmission testing into future epidemic estimates. PMID:28296801

Standard errors in forest area

Treesearch

Joseph McCollum

2002-01-01

I trace the development of standard error equations for forest area, beginning with the theory behind double sampling and the variance of a product. The discussion shifts to the particular problem of forest area - at which time the theory becomes relevant. There are subtle difficulties in figuring out which variance of a product equation should be used. The equations...

A log-sinh transformation for data normalization and variance stabilization

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Shrestha, D. L.; Robertson, D. E.; Pokhrel, P.

2012-05-01

When quantifying model prediction uncertainty, it is statistically convenient to represent model errors that are normally distributed with a constant variance. The Box-Cox transformation is the most widely used technique to normalize data and stabilize variance, but it is not without limitations. In this paper, a log-sinh transformation is derived based on a pattern of errors commonly seen in hydrological model predictions. It is suited to applications where prediction variables are positively skewed and the spread of errors is seen to first increase rapidly, then slowly, and eventually approach a constant as the prediction variable becomes greater. The log-sinh transformation is applied in two case studies, and the results are compared with one- and two-parameter Box-Cox transformations.

An analytic technique for statistically modeling random atomic clock errors in estimation

NASA Technical Reports Server (NTRS)

Fell, P. J.

1981-01-01

Minimum variance estimation requires that the statistics of random observation errors be modeled properly. If measurements are derived through the use of atomic frequency standards, then one source of error affecting the observable is random fluctuation in frequency. This is the case, for example, with range and integrated Doppler measurements from satellites of the Global Positioning and baseline determination for geodynamic applications. An analytic method is presented which approximates the statistics of this random process. The procedure starts with a model of the Allan variance for a particular oscillator and develops the statistics of range and integrated Doppler measurements. A series of five first order Markov processes is used to approximate the power spectral density obtained from the Allan variance.

Assumption-free estimation of the genetic contribution to refractive error across childhood.

PubMed

Guggenheim, Jeremy A; St Pourcain, Beate; McMahon, George; Timpson, Nicholas J; Evans, David M; Williams, Cathy

2015-01-01

Studies in relatives have generally yielded high heritability estimates for refractive error: twins 75-90%, families 15-70%. However, because related individuals often share a common environment, these estimates are inflated (via misallocation of unique/common environment variance). We calculated a lower-bound heritability estimate for refractive error free from such bias. Between the ages 7 and 15 years, participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) underwent non-cycloplegic autorefraction at regular research clinics. At each age, an estimate of the variance in refractive error explained by single nucleotide polymorphism (SNP) genetic variants was calculated using genome-wide complex trait analysis (GCTA) using high-density genome-wide SNP genotype information (minimum N at each age=3,404). The variance in refractive error explained by the SNPs ("SNP heritability") was stable over childhood: Across age 7-15 years, SNP heritability averaged 0.28 (SE=0.08, p<0.001). The genetic correlation for refractive error between visits varied from 0.77 to 1.00 (all p<0.001) demonstrating that a common set of SNPs was responsible for the genetic contribution to refractive error across this period of childhood. Simulations suggested lack of cycloplegia during autorefraction led to a small underestimation of SNP heritability (adjusted SNP heritability=0.35; SE=0.09). To put these results in context, the variance in refractive error explained (or predicted) by the time participants spent outdoors was <0.005 and by the time spent reading was <0.01, based on a parental questionnaire completed when the child was aged 8-9 years old. Genetic variation captured by common SNPs explained approximately 35% of the variation in refractive error between unrelated subjects. This value sets an upper limit for predicting refractive error using existing SNP genotyping arrays, although higher-density genotyping in larger samples and inclusion of interaction effects is expected to raise this figure toward twin- and family-based heritability estimates. The same SNPs influenced refractive error across much of childhood. Notwithstanding the strong evidence of association between time outdoors and myopia, and time reading and myopia, less than 1% of the variance in myopia at age 15 was explained by crude measures of these two risk factors, indicating that their effects may be limited, at least when averaged over the whole population.

Errors in radial velocity variance from Doppler wind lidar

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

Wang, H.; Barthelmie, R. J.; Doubrawa, P.

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Errors in radial velocity variance from Doppler wind lidar

DOE PAGES

Wang, H.; Barthelmie, R. J.; Doubrawa, P.; ...

2016-08-29

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

On the impact of relatedness on SNP association analysis.

PubMed

Gross, Arnd; Tönjes, Anke; Scholz, Markus

2017-12-06

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness. We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation. Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.

On the problem of data assimilation by means of synchronization

NASA Astrophysics Data System (ADS)

Szendro, Ivan G.; RodríGuez, Miguel A.; López, Juan M.

2009-10-01

The potential use of synchronization as a method for data assimilation is investigated in a Lorenz96 model. Data representing the reality are obtained from a Lorenz96 model with added noise. We study the assimilation scheme by means of synchronization for different noise intensities. We use a novel plot representation of the synchronization error in a phase diagram consisting of two variables: the amplitude and the width of the error after a suitable logarithmic transformation (the so-called mean-variance of logarithms diagram). Our main result concerns the existence of an "optimal" coupling for which the synchronization is maximal. We finally show how this allows us to quantify the degree of assimilation, providing a criterion for the selection of optimal couplings and validity of models.

Frequency domain analysis of errors in cross-correlations of ambient seismic noise

NASA Astrophysics Data System (ADS)

Liu, Xin; Ben-Zion, Yehuda; Zigone, Dimitri

2016-12-01

We analyse random errors (variances) in cross-correlations of ambient seismic noise in the frequency domain, which differ from previous time domain methods. Extending previous theoretical results on ensemble averaged cross-spectrum, we estimate confidence interval of stacked cross-spectrum of finite amount of data at each frequency using non-overlapping windows with fixed length. The extended theory also connects amplitude and phase variances with the variance of each complex spectrum value. Analysis of synthetic stationary ambient noise is used to estimate the confidence interval of stacked cross-spectrum obtained with different length of noise data corresponding to different number of evenly spaced windows of the same duration. This method allows estimating Signal/Noise Ratio (SNR) of noise cross-correlation in the frequency domain, without specifying filter bandwidth or signal/noise windows that are needed for time domain SNR estimations. Based on synthetic ambient noise data, we also compare the probability distributions, causal part amplitude and SNR of stacked cross-spectrum function using one-bit normalization or pre-whitening with those obtained without these pre-processing steps. Natural continuous noise records contain both ambient noise and small earthquakes that are inseparable from the noise with the existing pre-processing steps. Using probability distributions of random cross-spectrum values based on the theoretical results provides an effective way to exclude such small earthquakes, and additional data segments (outliers) contaminated by signals of different statistics (e.g. rain, cultural noise), from continuous noise waveforms. This technique is applied to constrain values and uncertainties of amplitude and phase velocity of stacked noise cross-spectrum at different frequencies, using data from southern California at both regional scale (˜35 km) and dense linear array (˜20 m) across the plate-boundary faults. A block bootstrap resampling method is used to account for temporal correlation of noise cross-spectrum at low frequencies (0.05-0.2 Hz) near the ocean microseismic peaks.

Dynamic shaping of dopamine signals during probabilistic Pavlovian conditioning.

PubMed

Hart, Andrew S; Clark, Jeremy J; Phillips, Paul E M

2015-01-01

Cue- and reward-evoked phasic dopamine activity during Pavlovian and operant conditioning paradigms is well correlated with reward-prediction errors from formal reinforcement learning models, which feature teaching signals in the form of discrepancies between actual and expected reward outcomes. Additionally, in learning tasks where conditioned cues probabilistically predict rewards, dopamine neurons show sustained cue-evoked responses that are correlated with the variance of reward and are maximal to cues predicting rewards with a probability of 0.5. Therefore, it has been suggested that sustained dopamine activity after cue presentation encodes the uncertainty of impending reward delivery. In the current study we examined the acquisition and maintenance of these neural correlates using fast-scan cyclic voltammetry in rats implanted with carbon fiber electrodes in the nucleus accumbens core during probabilistic Pavlovian conditioning. The advantage of this technique is that we can sample from the same animal and recording location throughout learning with single trial resolution. We report that dopamine release in the nucleus accumbens core contains correlates of both expected value and variance. A quantitative analysis of these signals throughout learning, and during the ongoing updating process after learning in probabilistic conditions, demonstrates that these correlates are dynamically encoded during these phases. Peak CS-evoked responses are correlated with expected value and predominate during early learning while a variance-correlated sustained CS signal develops during the post-asymptotic updating phase. Copyright © 2014 Elsevier Inc. All rights reserved.

A note on variance estimation in random effects meta-regression.

PubMed

Sidik, Kurex; Jonkman, Jeffrey N

2005-01-01

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

Hedonic price models with omitted variables and measurement errors: a constrained autoregression-structural equation modeling approach with application to urban Indonesia

NASA Astrophysics Data System (ADS)

Suparman, Yusep; Folmer, Henk; Oud, Johan H. L.

2014-01-01

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression-structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

Statistical model to perform error analysis of curve fits of wind tunnel test data using the techniques of analysis of variance and regression analysis

NASA Technical Reports Server (NTRS)

Alston, D. W.

1981-01-01

The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2016-01-01

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

DPLL implementation in carrier acquisition and tracking for burst DS-CDMA receivers.

PubMed

Guan, Yun-feng; Zhang, Zhao-yang; Lai, Li-feng

2003-01-01

This paper presents the architectures, algorithms, and implementation considerations of the digital phase locked loop (DPLL) used for burst-mode packet DS-CDMA receivers. As we know, carrier offset is a rather challenging problem in CDMA system. According to different applications, different DPLL forms should be adopted to correct different maximum carrier offset in CDMA systems. One classical DPLL and two novel DPLL forms are discussed in the paper. The acquisition range of carrier offset can be widened by using the two novel DPLL forms without any performance degradation such as longer acquisition time or larger variance of the phase error. The maximum acquisition range is 1/(4T), where T is the symbol period. The design can be implemented by FPGA directly.

Mixture-mixture design for the fingerprint optimization of chromatographic mobile phases and extraction solutions for Camellia sinensis.

PubMed

Borges, Cleber N; Bruns, Roy E; Almeida, Aline A; Scarminio, Ieda S

2007-07-09

A composite simplex centroid-simplex centroid mixture design is proposed for simultaneously optimizing two mixture systems. The complementary model is formed by multiplying special cubic models for the two systems. The design was applied to the simultaneous optimization of both mobile phase chromatographic mixtures and extraction mixtures for the Camellia sinensis Chinese tea plant. The extraction mixtures investigated contained varying proportions of ethyl acetate, ethanol and dichloromethane while the mobile phase was made up of varying proportions of methanol, acetonitrile and a methanol-acetonitrile-water (MAW) 15%:15%:70% mixture. The experiments were block randomized corresponding to a split-plot error structure to minimize laboratory work and reduce environmental impact. Coefficients of an initial saturated model were obtained using Scheffe-type equations. A cumulative probability graph was used to determine an approximate reduced model. The split-plot error structure was then introduced into the reduced model by applying generalized least square equations with variance components calculated using the restricted maximum likelihood approach. A model was developed to calculate the number of peaks observed with the chromatographic detector at 210 nm. A 20-term model contained essentially all the statistical information of the initial model and had a root mean square calibration error of 1.38. The model was used to predict the number of peaks eluted in chromatograms obtained from extraction solutions that correspond to axial points of the simplex centroid design. The significant model coefficients are interpreted in terms of interacting linear, quadratic and cubic effects of the mobile phase and extraction solution components.

Design of Instrument Dials for Maximum Legibility. Part 5. Origin Location, Scale Break, Number Location, and Contrast Direction

DTIC Science & Technology

1951-05-01

prccedur&:s to be of hipn accuracy. Ambij;uity of subject responizes due to overlap of entries on tU,, record sheets vas negligible. Handwriting ...experimental variables on reading errors us carried out by analysis of variance methods. For this purpose it was convenient to consider different classes...on any scale - an error ofY one numbered division. For this reason, the result. of the analysis of variance of the /10’s errors by dial types may

Commonly Unrecognized Error Variance in Statewide Assessment Programs: Sources of Error Variance and What Can Be Done to Reduce Them

ERIC Educational Resources Information Center

Brockmann, Frank

2011-01-01

State testing programs today are more extensive than ever, and their results are required to serve more purposes and high-stakes decisions than one might have imagined. Assessment results are used to hold schools, districts, and states accountable for student performance and to help guide a multitude of important decisions. This report describes…

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.

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

NASA Technical Reports Server (NTRS)

Tompkins, Stefanie; Mustard, John F.; Pieters, Carle M.; Forsyth, Donald W.

1993-01-01

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

In vivo validation of a new technique that compensates for soft tissue artefact in the upper-arm: preliminary results.

PubMed

Cutti, Andrea Giovanni; Cappello, Angelo; Davalli, Angelo

2006-01-01

Soft tissue artefact is the dominant error source for upper extremity motion analyses that use skin-mounted markers, especially in humeral axial rotation. A new in vivo technique is presented that is based on the definition of a humerus bone-embedded frame almost "artefact free" but influenced by the elbow orientation in the measurement of the humeral axial rotation, and on an algorithm designed to solve this kinematic coupling. The technique was validated in vivo in a study of six healthy subjects who performed five arm-movement tasks. For each task the similarity between a gold standard pattern and the axial rotation pattern before and after the application of the compensation algorithm was evaluated in terms of explained variance, gain, phase and offset. In addition the root mean square error between the patterns was used as a global similarity estimator. After the application, for four out of five tasks, patterns were highly correlated, in phase, with almost equal gain and limited offset; the root mean square error decreased from the original 9 degrees to 3 degrees . The proposed technique appears to help compensate for the soft tissue artefact affecting axial rotation. A further development is also proposed to make the technique effective also for the pure prono-supination task.

Statistically Self-Consistent and Accurate Errors for SuperDARN Data

NASA Astrophysics Data System (ADS)

Reimer, A. S.; Hussey, G. C.; McWilliams, K. A.

2018-01-01

The Super Dual Auroral Radar Network (SuperDARN)-fitted data products (e.g., spectral width and velocity) are produced using weighted least squares fitting. We present a new First-Principles Fitting Methodology (FPFM) that utilizes the first-principles approach of Reimer et al. (2016) to estimate the variance of the real and imaginary components of the mean autocorrelation functions (ACFs) lags. SuperDARN ACFs fitted by the FPFM do not use ad hoc or empirical criteria. Currently, the weighting used to fit the ACF lags is derived from ad hoc estimates of the ACF lag variance. Additionally, an overcautious lag filtering criterion is used that sometimes discards data that contains useful information. In low signal-to-noise (SNR) and/or low signal-to-clutter regimes the ad hoc variance and empirical criterion lead to underestimated errors for the fitted parameter because the relative contributions of signal, noise, and clutter to the ACF variance is not taken into consideration. The FPFM variance expressions include contributions of signal, noise, and clutter. The clutter is estimated using the maximal power-based self-clutter estimator derived by Reimer and Hussey (2015). The FPFM was successfully implemented and tested using synthetic ACFs generated with the radar data simulator of Ribeiro, Ponomarenko, et al. (2013). The fitted parameters and the fitted-parameter errors produced by the FPFM are compared with the current SuperDARN fitting software, FITACF. Using self-consistent statistical analysis, the FPFM produces reliable or trustworthy quantitative measures of the errors of the fitted parameters. For an SNR in excess of 3 dB and velocity error below 100 m/s, the FPFM produces 52% more data points than FITACF.

On the reach of perturbative descriptions for dark matter displacement fields

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

Baldauf, Tobias; Zaldarriaga, Matias; Schaan, Emmanuel, E-mail: baldauf@ias.edu, E-mail: eschaan@astro.princeton.edu, E-mail: matiasz@ias.edu

We study Lagrangian Perturbation Theory (LPT) and its regularization in the Effective Field Theory (EFT) approach. We evaluate the LPT displacement with the same phases as a corresponding N-body simulation, which allows us to compare perturbation theory to the non-linear simulation with significantly reduced cosmic variance, and provides a more stringent test than simply comparing power spectra. We reliably detect a non-vanishing leading order EFT coefficient and a stochastic displacement term, uncorrelated with the LPT terms. This stochastic term is expected in the EFT framework, and, to the best of our understanding, is not an artifact of numerical errors ormore » transients in our simulations. This term constitutes a limit to the accuracy of perturbative descriptions of the displacement field and its phases, corresponding to a 1% error on the non-linear power spectrum at k = 0.2 h{sup −1}Mpc at z = 0. Predicting the displacement power spectrum to higher accuracy or larger wavenumbers thus requires a model for the stochastic displacement.« less

Prediction of peak shape in hydro-organic and micellar-organic liquid chromatography as a function of mobile phase composition.

PubMed

Baeza-Baeza, J J; Ruiz-Angel, M J; García-Alvarez-Coque, M C

2007-09-07

A simple model is proposed that relates the parameters describing the peak width with the retention time, which can be easily predicted as a function of mobile phase composition. This allows the further prediction of peak shape with global errors below 5%, using a modified Gaussian model with a parabolic variance. The model is useful in the optimisation of chromatographic resolution to assess an eventual overlapping of close peaks. The dependence of peak shape with mobile phase composition was studied for mobile phases containing acetonitrile in the presence and absence of micellised surfactant (micellar-organic and hydro-organic reversed-phase liquid chromatography, RPLC). In micellar RPLC, both modifiers (surfactant and acetonitrile) were observed to decrease or improve the efficiencies in the same percentage, at least in the studied concentration ranges. The study also revealed that the problem of achieving smaller efficiencies in this chromatographic mode, compared to hydro-organic RPLC, is not only related to the presence of surfactant covering the stationary phase, but also to the smaller concentration of organic solvent in the mobile phase.

Noise in x-ray grating-based phase-contrast imaging.

PubMed

Weber, Thomas; Bartl, Peter; Bayer, Florian; Durst, Jürgen; Haas, Wilhelm; Michel, Thilo; Ritter, André; Anton, Gisela

2011-07-01

Grating-based x-ray phase-contrast imaging is a fast developing new modality not only for medical imaging, but as well for other fields such as material sciences. While these many possible applications arise, the knowledge of the noise behavior is essential. In this work, the authors used a least squares fitting algorithm to calculate the noise behavior of the three quantities absorption, differential phase, and dark-field image. Further, the calculated error formula of the differential phase image was verified by measurements. Therefore, a Talbot interferometer was setup, using a microfocus x-ray tube as source and a Timepix detector for photon counting. Additionally, simulations regarding this topic were performed. It turned out that the variance of the reconstructed phase is only dependent of the total number of photons used to generate the phase image and the visibility of the experimental setup. These results could be evaluated in measurements as well as in simulations. Furthermore, the correlation between absorption and dark-field image was calculated. These results provide the understanding of the noise characteristics of grating-based phase-contrast imaging and will help to improve image quality.

Fitting dynamic models to the Geosat sea level observations in the tropical Pacific Ocean. I - A free wave model

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Vazquez, Jorge; Perigaud, Claire

1991-01-01

Free, equatorially trapped sinusoidal wave solutions to a linear model on an equatorial beta plane are used to fit the Geosat altimetric sea level observations in the tropical Pacific Ocean. The Kalman filter technique is used to estimate the wave amplitude and phase from the data. The estimation is performed at each time step by combining the model forecast with the observation in an optimal fashion utilizing the respective error covariances. The model error covariance is determined such that the performance of the model forecast is optimized. It is found that the dominant observed features can be described qualitatively by basin-scale Kelvin waves and the first meridional-mode Rossby waves. Quantitatively, however, only 23 percent of the signal variance can be accounted for by this simple model.

Effects of diffraction by ionospheric electron density irregularities on the range error in GNSS dual-frequency positioning and phase decorrelation

NASA Astrophysics Data System (ADS)

Gherm, Vadim E.; Zernov, Nikolay N.; Strangeways, Hal J.

2011-06-01

It can be important to determine the correlation of different frequency signals in L band that have followed transionospheric paths. In the future, both GPS and the new Galileo satellite system will broadcast three frequencies enabling more advanced three frequency correction schemes so that knowledge of correlations of different frequency pairs for scintillation conditions is desirable. Even at present, it would be helpful to know how dual-frequency Global Navigation Satellite Systems positioning can be affected by lack of correlation between the L1 and L2 signals. To treat this problem of signal correlation for the case of strong scintillation, a previously constructed simulator program, based on the hybrid method, has been further modified to simulate the fields for both frequencies on the ground, taking account of their cross correlation. Then, the errors in the two-frequency range finding method caused by scintillation have been estimated for particular ionospheric conditions and for a realistic fully three-dimensional model of the ionospheric turbulence. The results which are presented for five different frequency pairs (L1/L2, L1/L3, L1/L5, L2/L3, and L2/L5) show the dependence of diffractional errors on the scintillation index S4 and that the errors diverge from a linear relationship, the stronger are scintillation effects, and may reach up to ten centimeters, or more. The correlation of the phases at spaced frequencies has also been studied and found that the correlation coefficients for different pairs of frequencies depend on the procedure of phase retrieval, and reduce slowly as both the variance of the electron density fluctuations and cycle slips increase.

Improved Event Location Uncertainty Estimates

DTIC Science & Technology

2008-06-30

throughout this study . The data set consists of GT0-2 nuclear explosions from the SAIC Nuclear Explosion Database (www.rdss.info, Bahavar et al...errors: Bias and variance In this study SNR dependence of both delay and variance of reading errors of first arriving P waves are analyzed and...ground truth and range of event size. For other datasets we turn to estimates based on double- differences between arrival times of station pairs

Estimation and Simulation of Slow Crack Growth Parameters from Constant Stress Rate Data

NASA Technical Reports Server (NTRS)

Salem, Jonathan A.; Weaver, Aaron S.

2003-01-01

Closed form, approximate functions for estimating the variances and degrees-of-freedom associated with the slow crack growth parameters n, D, B, and A(sup *) as measured using constant stress rate ('dynamic fatigue') testing were derived by using propagation of errors. Estimates made with the resulting functions and slow crack growth data for a sapphire window were compared to the results of Monte Carlo simulations. The functions for estimation of the variances of the parameters were derived both with and without logarithmic transformation of the initial slow crack growth equations. The transformation was performed to make the functions both more linear and more normal. Comparison of the Monte Carlo results and the closed form expressions derived with propagation of errors indicated that linearization is not required for good estimates of the variances of parameters n and D by the propagation of errors method. However, good estimates variances of the parameters B and A(sup *) could only be made when the starting slow crack growth equation was transformed and the coefficients of variation of the input parameters were not too large. This was partially a result of the skewered distributions of B and A(sup *). Parametric variation of the input parameters was used to determine an acceptable range for using closed form approximate equations derived from propagation of errors.

Evaluation of the impact of observations on blended sea surface winds in a two-dimensional variational scheme using degrees of freedom

NASA Astrophysics Data System (ADS)

Wang, Ting; Xiang, Jie; Fei, Jianfang; Wang, Yi; Liu, Chunxia; Li, Yuanxiang

2017-12-01

This paper presents an evaluation of the observational impacts on blended sea surface winds from a two-dimensional variational data assimilation (2D-Var) scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal (DFS), and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two methods, a priori and a posteriori, are used to estimate the DFS of the zonal ( u) and meridional ( v) components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is reduced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.

Precision of Four Acoustic Bone Measurement Devices

NASA Technical Reports Server (NTRS)

Miller, Christopher; Feiveson, Alan H.; Shackelford, Linda; Rianon, Nahida; LeBlanc, Adrian

2000-01-01

Though many studies have quantified the precision of various acoustic bone measurement devices, it is difficult to directly compare the results among the studies, because they used disparate subject pools, did not specify the estimation methodology, or did not use consistent definitions for various precision characteristics. In this study, we used a repeated measures design protocol to directly determine the precision characteristics of four acoustic bone measurement devices: the Mechanical Response Tissue Analyzer (MRTA), the UBA-575+, the SoundScan 2000 (S2000), and the Sahara Ultrasound Done Analyzer. Ten men and ten women were scanned on all four devices by two different operators at five discrete time points: Week 1, Week 2, Week 3, Month 3 and Month 6. The percent coefficient of variation (%CV) and standardized coefficient of variation were computed for the following precision characteristics: interoperator effect, operator-subject interaction, short-term error variance, and long-term drift, The MRTA had high interoperator errors for its ulnar and tibial stiffness measures and a large long-term drift in its tibial stiffness measurement. The UBA-575+ exhibited large short-term error variances and long-term drift for all three of its measurements. The S2000's tibial speed of sound measurement showed a high short-term error variance and a significant operator-subject interaction but very good values ( < 1%) for the other precision characteristics. The Sahara seemed to have the best overall performance, but was hampered by a large %CV for short-term error variance in its broadband ultrasound attenuation measure.

Precision of Four Acoustic Bone Measurement Devices

NASA Technical Reports Server (NTRS)

Miller, Christopher; Rianon, Nahid; Feiveson, Alan; Shackelford, Linda; LeBlanc, Adrian

2000-01-01

Though many studies have quantified the precision of various acoustic bone measurement devices, it is difficult to directly compare the results among the studies, because they used disparate subject pools, did not specify the estimation methodology, or did not use consistent definitions for various precision characteristics. In this study, we used a repeated measures design protocol to directly determine the precision characteristics of four acoustic bone measurement devices: the Mechanical Response Tissue Analyzer (MRTA), the UBA-575+, the SoundScan 2000 (S2000), and the Sahara Ultrasound Bone Analyzer. Ten men and ten women were scanned on all four devices by two different operators at five discrete time points: Week 1, Week 2, Week 3, Month 3 and Month 6. The percent coefficient of variation (%CV) and standardized coefficient of variation were computed for the following precision characteristics: interoperator effect, operator-subject interaction, short-term error variance, and long-term drift. The MRTA had high interoperator errors for its ulnar and tibial stiffness measures and a large long-term drift in its tibial stiffness measurement. The UBA-575+ exhibited large short-term error variances and long-term drift for all three of its measurements. The S2000's tibial speed of sound measurement showed a high short-term error variance and a significant operator-subject interaction but very good values (less than 1%) for the other precision characteristics. The Sahara seemed to have the best overall performance, but was hampered by a large %CV for short-term error variance in its broadband ultrasound attenuation measure.

Adaptive Kalman filter based on variance component estimation for the prediction of ionospheric delay in aiding the cycle slip repair of GNSS triple-frequency signals

NASA Astrophysics Data System (ADS)

Chang, Guobin; Xu, Tianhe; Yao, Yifei; Wang, Qianxin

2018-01-01

In order to incorporate the time smoothness of ionospheric delay to aid the cycle slip detection, an adaptive Kalman filter is developed based on variance component estimation. The correlations between measurements at neighboring epochs are fully considered in developing a filtering algorithm for colored measurement noise. Within this filtering framework, epoch-differenced ionospheric delays are predicted. Using this prediction, the potential cycle slips are repaired for triple-frequency signals of global navigation satellite systems. Cycle slips are repaired in a stepwise manner; i.e., for two extra wide lane combinations firstly and then for the third frequency. In the estimation for the third frequency, a stochastic model is followed in which the correlations between the ionospheric delay prediction errors and the errors in the epoch-differenced phase measurements are considered. The implementing details of the proposed method are tabulated. A real BeiDou Navigation Satellite System data set is used to check the performance of the proposed method. Most cycle slips, no matter trivial or nontrivial, can be estimated in float values with satisfactorily high accuracy and their integer values can hence be correctly obtained by simple rounding. To be more specific, all manually introduced nontrivial cycle slips are correctly repaired.

Complex differential variance angiography with noise-bias correction for optical coherence tomography of the retina

PubMed Central

Braaf, Boy; Donner, Sabine; Nam, Ahhyun S.; Bouma, Brett E.; Vakoc, Benjamin J.

2018-01-01

Complex differential variance (CDV) provides phase-sensitive angiographic imaging for optical coherence tomography (OCT) with immunity to phase-instabilities of the imaging system and small-scale axial bulk motion. However, like all angiographic methods, measurement noise can result in erroneous indications of blood flow that confuse the interpretation of angiographic images. In this paper, a modified CDV algorithm that corrects for this noise-bias is presented. This is achieved by normalizing the CDV signal by analytically derived upper and lower limits. The noise-bias corrected CDV algorithm was implemented into an experimental 1 μm wavelength OCT system for retinal imaging that used an eye tracking scanner laser ophthalmoscope at 815 nm for compensation of lateral eye motions. The noise-bias correction improved the CDV imaging of the blood flow in tissue layers with a low signal-to-noise ratio and suppressed false indications of blood flow outside the tissue. In addition, the CDV signal normalization suppressed noise induced by galvanometer scanning errors and small-scale lateral motion. High quality cross-section and motion-corrected en face angiograms of the retina and choroid are presented. PMID:29552388

Complex differential variance angiography with noise-bias correction for optical coherence tomography of the retina.

PubMed

Braaf, Boy; Donner, Sabine; Nam, Ahhyun S; Bouma, Brett E; Vakoc, Benjamin J

2018-02-01

Complex differential variance (CDV) provides phase-sensitive angiographic imaging for optical coherence tomography (OCT) with immunity to phase-instabilities of the imaging system and small-scale axial bulk motion. However, like all angiographic methods, measurement noise can result in erroneous indications of blood flow that confuse the interpretation of angiographic images. In this paper, a modified CDV algorithm that corrects for this noise-bias is presented. This is achieved by normalizing the CDV signal by analytically derived upper and lower limits. The noise-bias corrected CDV algorithm was implemented into an experimental 1 μm wavelength OCT system for retinal imaging that used an eye tracking scanner laser ophthalmoscope at 815 nm for compensation of lateral eye motions. The noise-bias correction improved the CDV imaging of the blood flow in tissue layers with a low signal-to-noise ratio and suppressed false indications of blood flow outside the tissue. In addition, the CDV signal normalization suppressed noise induced by galvanometer scanning errors and small-scale lateral motion. High quality cross-section and motion-corrected en face angiograms of the retina and choroid are presented.

Spectral analysis of the Earth's topographic potential via 2D-DFT: a new data-based degree variance model to degree 90,000

NASA Astrophysics Data System (ADS)

Rexer, Moritz; Hirt, Christian

2015-09-01

Classical degree variance models (such as Kaula's rule or the Tscherning-Rapp model) often rely on low-resolution gravity data and so are subject to extrapolation when used to describe the decay of the gravity field at short spatial scales. This paper presents a new degree variance model based on the recently published GGMplus near-global land areas 220 m resolution gravity maps (Geophys Res Lett 40(16):4279-4283, 2013). We investigate and use a 2D-DFT (discrete Fourier transform) approach to transform GGMplus gravity grids into degree variances. The method is described in detail and its approximation errors are studied using closed-loop experiments. Focus is placed on tiling, azimuth averaging, and windowing effects in the 2D-DFT method and on analytical fitting of degree variances. Approximation errors of the 2D-DFT procedure on the (spherical harmonic) degree variance are found to be at the 10-20 % level. The importance of the reference surface (sphere, ellipsoid or topography) of the gravity data for correct interpretation of degree variance spectra is highlighted. The effect of the underlying mass arrangement (spherical or ellipsoidal approximation) on the degree variances is found to be crucial at short spatial scales. A rule-of-thumb for transformation of spectra between spherical and ellipsoidal approximation is derived. Application of the 2D-DFT on GGMplus gravity maps yields a new degree variance model to degree 90,000. The model is supported by GRACE, GOCE, EGM2008 and forward-modelled gravity at 3 billion land points over all land areas within the SRTM data coverage and provides gravity signal variances at the surface of the topography. The model yields omission errors of 9 mGal for gravity (1.5 cm for geoid effects) at scales of 10 km, 4 mGal (1 mm) at 2-km scales, and 2 mGal (0.2 mm) at 1-km scales.

Anticipatory synergy adjustments reflect individual performance of feedforward force control.

PubMed

Togo, Shunta; Imamizu, Hiroshi

2016-10-06

We grasp and dexterously manipulate an object through multi-digit synergy. In the framework of the uncontrolled manifold (UCM) hypothesis, multi-digit synergy is defined as the coordinated control mechanism of fingers to stabilize variable important for task success, e.g., total force. Previous studies reported anticipatory synergy adjustments (ASAs) that correspond to a drop of the synergy index before a quick change of the total force. The present study compared ASA's properties with individual performances of feedforward force control to investigate a relationship of those. Subjects performed a total finger force production task that consisted of a phase in which subjects tracked target line with visual information and a phase in which subjects produced total force pulse without visual information. We quantified their multi-digit synergy through UCM analysis and observed significant ASAs before producing total force pulse. The time of the ASA initiation and the magnitude of the drop of the synergy index were significantly correlated with the error of force pulse, but not with the tracking error. Almost all subjects showed a significant increase of the variance that affected the total force. Our study directly showed that ASA reflects the individual performance of feedforward force control independently of target-tracking performance and suggests that the multi-digit synergy was weakened to adjust the multi-digit movements based on a prediction error so as to reduce the future error. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Palmprint Based Multidimensional Fuzzy Vault Scheme

PubMed Central

Liu, Hailun; Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

2014-01-01

Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FVS to handle intraclass variances. Palmprint is one of the most important biometrics; to protect palmprint templates; a palmprint based MDFVS implementation is also presented. Experimental results show that the proposed scheme not only can deal with intraclass variances effectively but also could maintain the accuracy and meanwhile enhance security. PMID:24892094

Study on phase noise induced by 1/f noise of the modulator drive circuit in high-sensitivity fiber optic gyroscope

NASA Astrophysics Data System (ADS)

Teng, Fei; Jin, Jing; Li, Yong; Zhang, Chunxi

2018-05-01

The contribution of modulator drive circuit noise as a 1/f noise source to the output noise of the high-sensitivity interferometric fiber optic gyroscope (IFOG) was studied here. A noise model of closed-loop IFOG was built. By applying the simulated 1/f noise sequence into the model, a gyroscope output data series was acquired, and the corresponding power spectrum density (PSD) and the Allan variance curve were calculated to analyze the noise characteristic. The PSD curve was in the spectral shape of 1/f, which verifies that the modulator drive circuit induced a low frequency 1/f phase noise into the gyroscope. The random walk coefficient (RWC), a standard metric to characterize the noise performance of the IFOG, was calculated according to the Allan variance curve. Using an operational amplifier with an input 1/f noise of 520 nV/√Hz at 1 Hz, the RWC induced by this 1/f noise was 2 × 10-4°/√h, which accounts for 63% of the total RWC. To verify the correctness of the noise model we proposed, a high-sensitivity gyroscope prototype was built and tested. The simulated Allan variance curve gave a good rendition of the prototype actual measured curve. The error percentage between the simulated RWC and the measured value was less than 13%. According to the model, a noise reduction method is proposed and the effectiveness is verified by the experiment.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Improving lidar turbulence estimates for wind energy

NASA Astrophysics Data System (ADS)

Newman, J. F.; Clifton, A.; Churchfield, M. J.; Klein, P.

2016-09-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.

Improving Lidar Turbulence Estimates for Wind Energy: Preprint

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

Newman, Jennifer; Clifton, Andrew; Churchfield, Matthew

2016-10-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Improving Lidar Turbulence Estimates for Wind Energy

DOE PAGES

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; ...

2016-10-03

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Estimation of genetic parameters and their sampling variances of quantitative traits in the type 2 modified augmented design

USDA-ARS?s Scientific Manuscript database

We proposed a method to estimate the error variance among non-replicated genotypes, thus to estimate the genetic parameters by using replicated controls. We derived formulas to estimate sampling variances of the genetic parameters. Computer simulation indicated that the proposed methods of estimatin...

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

PubMed Central

Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao

2015-01-01

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. PMID:25625903

Bayesian inversions of a dynamic vegetation model in four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; François, L.

2015-01-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB dynamic vegetation model (DVM) with ten unknown parameters, using the DREAM(ZS) Markov chain Monte Carlo (MCMC) sampler. We compare model inversions considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a~priori or jointly inferred with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root-mean-square error (RMSE) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19 g C m-2 day-1, 1.04 to 1.56 g C m-2 day-1, and 0.50 to 1.28 mm day-1, respectively. In validation, mismatches between measured and simulated data are larger, but still with Nash-Sutcliffe efficiency scores above 0.5 for three out of the four sites. Although measurement errors associated with eddy covariance data are known to be heteroscedastic, we showed that assuming a classical linear heteroscedastic model of the residual errors in the inversion do not fully remove heteroscedasticity. Since the employed heteroscedastic error model allows for larger deviations between simulated and measured data as the magnitude of the measured data increases, this error model expectedly lead to poorer data fitting compared to inversions considering a constant variance of the residual errors. Furthermore, sampling the residual error variances along with model parameters results in overall similar model parameter posterior distributions as those obtained by fixing these variances beforehand, while slightly improving model performance. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides model behaviour, difference between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. Lastly, the possibility of finding a common set of parameters among the four experimental sites is discussed.

Compensation in the presence of deep turbulence using tiled-aperture architectures

NASA Astrophysics Data System (ADS)

Spencer, Mark F.; Brennan, Terry J.

2017-05-01

The presence of distributed-volume atmospheric aberrations or "deep turbulence" presents unique challenges for beam-control applications which look to sense and correct for disturbances found along the laser-propagation path. This paper explores the potential for branch-point-tolerant reconstruction algorithms and tiled-aperture architectures to correct for the branch cuts contained in the phase function due to deep-turbulence conditions. Using wave-optics simulations, the analysis aims to parameterize the fitting-error performance of tiled-aperture architectures operating in a null-seeking control loop with piston, tip, and tilt compensation of the individual optical beamlet trains. To evaluate fitting-error performance, the analysis plots normalized power in the bucket as a function of the Fried coherence diameter, the log-amplitude variance, and the number of subapertures for comparison purposes. Initial results show that tiled-aperture architectures with a large number of subapertures outperform filled-aperture architectures with continuous-face-sheet deformable mirrors.

Derivation of an analytic expression for the error associated with the noise reduction rating

NASA Astrophysics Data System (ADS)

Murphy, William J.

2005-04-01

Hearing protection devices are assessed using the Real Ear Attenuation at Threshold (REAT) measurement procedure for the purpose of estimating the amount of noise reduction provided when worn by a subject. The rating number provided on the protector label is a function of the mean and standard deviation of the REAT results achieved by the test subjects. If a group of subjects have a large variance, then it follows that the certainty of the rating should be correspondingly lower. No estimate of the error of a protector's rating is given by existing standards or regulations. Propagation of errors was applied to the Noise Reduction Rating to develop an analytic expression for the hearing protector rating error term. Comparison of the analytic expression for the error to the standard deviation estimated from Monte Carlo simulation of subject attenuations yielded a linear relationship across several protector types and assumptions for the variance of the attenuations.

Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis.

PubMed

Austin, Peter C

2016-12-30

Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model-based variance estimator; (ii) a robust sandwich-type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Environmental Influences on Well-Being: A Dyadic Latent Panel Analysis of Spousal Similarity

ERIC Educational Resources Information Center

Schimmack, Ulrich; Lucas, Richard E.

2010-01-01

This article uses dyadic latent panel analysis (DLPA) to examine environmental influences on well-being. DLPA requires longitudinal dyadic data. It decomposes the observed variance of both members of a dyad into a trait, state, and an error component. Furthermore, state variance is decomposed into initial and new state variance. Total observed…

An Application of Semi-parametric Estimator with Weighted Matrix of Data Depth in Variance Component Estimation

NASA Astrophysics Data System (ADS)

Pan, X. G.; Wang, J. Q.; Zhou, H. Y.

2013-05-01

The variance component estimation (VCE) based on semi-parametric estimator with weighted matrix of data depth has been proposed, because the coupling system model error and gross error exist in the multi-source heterogeneous measurement data of space and ground combined TT&C (Telemetry, Tracking and Command) technology. The uncertain model error has been estimated with the semi-parametric estimator model, and the outlier has been restrained with the weighted matrix of data depth. On the basis of the restriction of the model error and outlier, the VCE can be improved and used to estimate weighted matrix for the observation data with uncertain model error or outlier. Simulation experiment has been carried out under the circumstance of space and ground combined TT&C. The results show that the new VCE based on the model error compensation can determine the rational weight of the multi-source heterogeneous data, and restrain the outlier data.

Measuring the Utility of a Cyber Incident Mission Impact Assessment (CIMIA) Process for Mission Assurance

DTIC Science & Technology

2011-03-01

1.179 1 22 .289 POP-UP .000 1 22 .991 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design ...POP-UP 2.104 1 22 .161 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design : Intercept... design also limited the number of intended treatments. The experimental design originally was suppose to test all three adverse events that threaten

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005-2015

NASA Astrophysics Data System (ADS)

Thompson, David R.; Kahn, Brian H.; Green, Robert O.; Chien, Steve A.; Middleton, Elizabeth M.; Tran, Daniel Q.

2018-02-01

The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

Noise in x-ray grating-based phase-contrast imaging

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

Weber, Thomas; Bartl, Peter; Bayer, Florian

Purpose: Grating-based x-ray phase-contrast imaging is a fast developing new modality not only for medical imaging, but as well for other fields such as material sciences. While these many possible applications arise, the knowledge of the noise behavior is essential. Methods: In this work, the authors used a least squares fitting algorithm to calculate the noise behavior of the three quantities absorption, differential phase, and dark-field image. Further, the calculated error formula of the differential phase image was verified by measurements. Therefore, a Talbot interferometer was setup, using a microfocus x-ray tube as source and a Timepix detector for photonmore » counting. Additionally, simulations regarding this topic were performed. Results: It turned out that the variance of the reconstructed phase is only dependent of the total number of photons used to generate the phase image and the visibility of the experimental setup. These results could be evaluated in measurements as well as in simulations. Furthermore, the correlation between absorption and dark-field image was calculated. Conclusions: These results provide the understanding of the noise characteristics of grating-based phase-contrast imaging and will help to improve image quality.« less

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, Hong Yi; Milne, Alice; Webster, Richard

2016-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σK2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR = MSE/σK2 ≈ 1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (ECa) of the topsoil was measured at 525 points in a field of 2.3 ha. The marginal distribution of the observations was strongly positively skewed, and so the observed ECas were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, HongYi; Milne, Alice; Webster, Richard

2015-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σ_K^2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR=MSE/ σ_K2 ≈1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (EC_a) of the topsoil was measured at 525 points in a field of 2.3~ha. The marginal distribution of the observations was strongly positively skewed, and so the observed EC_as were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Performance of the all-digital data-transition tracking loop in the advanced receiver

NASA Astrophysics Data System (ADS)

Cheng, U.; Hinedi, S.

1989-11-01

The performance of the all-digital data-transition tracking loop (DTTL) with coherent or noncoherent sampling is described. The effects of few samples per symbol and of noncommensurate sampling rates and symbol rates are addressed and analyzed. Their impacts on the loop phase-error variance and the mean time to lose lock (MTLL) are quantified through computer simulations. The analysis and preliminary simulations indicate that with three to four samples per symbol, the DTTL can track with negligible jitter because of the presence of earth Doppler rate. Furthermore, the MTLL is also expected to be large engough to maintain lock over a Deep Space Network track.

Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

PubMed

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

PubMed Central

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W.; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. PMID:23844016

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

NASA Astrophysics Data System (ADS)

Rock, N. M. S.; Duffy, T. R.

REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.

Using Generalizability Theory to Examine Sources of Variance in Observed Behaviors within High School Classrooms

ERIC Educational Resources Information Center

Abry, Tashia; Cash, Anne H.; Bradshaw, Catherine P.

2014-01-01

Generalizability theory (GT) offers a useful framework for estimating the reliability of a measure while accounting for multiple sources of error variance. The purpose of this study was to use GT to examine multiple sources of variance in and the reliability of school-level teacher and high school student behaviors as observed using the tool,…

Consequences of Assumption Violations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance "F" Test.

ERIC Educational Resources Information Center

Lix, Lisa M.; And Others

1996-01-01

Meta-analytic techniques were used to summarize the statistical robustness literature on Type I error properties of alternatives to the one-way analysis of variance "F" test. The James (1951) and Welch (1951) tests performed best under violations of the variance homogeneity assumption, although their use is not always appropriate. (SLD)

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Emotion perception, non-social cognition and symptoms as predictors of theory of mind in schizophrenia.

PubMed

Vaskinn, Anja; Andersson, Stein; Østefjells, Tiril; Andreassen, Ole A; Sundet, Kjetil

2018-06-05

Theory of mind (ToM) can be divided into cognitive and affective ToM, and a distinction can be made between overmentalizing and undermentalizing errors. Research has shown that ToM in schizophrenia is associated with non-social and social cognition, and with clinical symptoms. In this study, we investigate cognitive and clinical predictors of different ToM processes. Ninety-one individuals with schizophrenia participated. ToM was measured with the Movie for the Assessment of Social Cognition (MASC) yielding six scores (total ToM, cognitive ToM, affective ToM, overmentalizing errors, undermentalizing errors and no mentalizing errors). Neurocognition was indexed by a composite score based on the non-social cognitive tests in the MATRICS Consensus Cognitive Battery (MCCB). Emotion perception was measured with Emotion in Biological Motion (EmoBio), a point-light walker task. Clinical symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS). Seventy-one healthy control (HC) participants completed the MASC. Individuals with schizophrenia showed large impairments compared to HC for all MASC scores, except overmentalizing errors. Hierarchical regression analyses with the six different MASC scores as dependent variables revealed that MCCB was a significant predictor of all MASC scores, explaining 8-18% of the variance. EmoBio increased the explained variance significantly, to 17-28%, except for overmentalizing errors. PANSS excited symptoms increased explained variance for total ToM, affective ToM and no mentalizing errors. Both social and non-social cognition were significant predictors of ToM. Overmentalizing was only predicted by non-social cognition. Excited symptoms contributed to overall and affective ToM, and to no mentalizing errors. Copyright © 2018 Elsevier Inc. All rights reserved.

Quantitative comparison of tympanic membrane displacements using two optical methods to recover the optical phase

NASA Astrophysics Data System (ADS)

Santiago-Lona, Cynthia V.; Hernández-Montes, María del Socorro; Mendoza-Santoyo, Fernando; Esquivel-Tejeda, Jesús

2018-02-01

The study and quantification of the tympanic membrane (TM) displacements add important information to advance the knowledge about the hearing process. A comparative statistical analysis between two commonly used demodulation methods employed to recover the optical phase in digital holographic interferometry, namely the fast Fourier transform and phase-shifting interferometry, is presented as applied to study thin tissues such as the TM. The resulting experimental TM surface displacement data are used to contrast both methods through the analysis of variance and F tests. Data are gathered when the TMs are excited with continuous sound stimuli at levels 86, 89 and 93 dB SPL for the frequencies of 800, 1300 and 2500 Hz under the same experimental conditions. The statistical analysis shows repeatability in z-direction displacements with a standard deviation of 0.086, 0.098 and 0.080 μm using the Fourier method, and 0.080, 0.104 and 0.055 μm with the phase-shifting method at a 95% confidence level for all frequencies. The precision and accuracy are evaluated by means of the coefficient of variation; the results with the Fourier method are 0.06143, 0.06125, 0.06154 and 0.06154, 0.06118, 0.06111 with phase-shifting. The relative error between both methods is 7.143, 6.250 and 30.769%. On comparing the measured displacements, the results indicate that there is no statistically significant difference between both methods for frequencies at 800 and 1300 Hz; however, errors and other statistics increase at 2500 Hz.

Experimental study on an FBG strain sensor

NASA Astrophysics Data System (ADS)

Liu, Hong-lin; Zhu, Zheng-wei; Zheng, Yong; Liu, Bang; Xiao, Feng

2018-01-01

Landslides and other geological disasters occur frequently and often cause high financial and humanitarian cost. The real-time, early-warning monitoring of landslides has important significance in reducing casualties and property losses. In this paper, by taking the high initial precision and high sensitivity advantage of FBG, an FBG strain sensor is designed combining FBGs with inclinometer. The sensor was regarded as a cantilever beam with one end fixed. According to the anisotropic material properties of the inclinometer, a theoretical formula between the FBG wavelength and the deflection of the sensor was established using the elastic mechanics principle. Accuracy of the formula established had been verified through laboratory calibration testing and model slope monitoring experiments. The displacement of landslide could be calculated by the established theoretical formula using the changing values of FBG central wavelength obtained by the demodulation instrument remotely. Results showed that the maximum error at different heights was 9.09%; the average of the maximum error was 6.35%, and its corresponding variance was 2.12; the minimum error was 4.18%; the average of the minimum error was 5.99%, and its corresponding variance was 0.50. The maximum error of the theoretical and the measured displacement decrease gradually, and the variance of the error also decreases gradually. This indicates that the theoretical results are more and more reliable. It also shows that the sensor and the theoretical formula established in this paper can be used for remote, real-time, high precision and early warning monitoring of the slope.

Evaluating causes of error in landmark-based data collection using scanners

PubMed Central

Shearer, Brian M.; Cooke, Siobhán B.; Halenar, Lauren B.; Reber, Samantha L.; Plummer, Jeannette E.; Delson, Eric

2017-01-01

In this study, we assess the precision, accuracy, and repeatability of craniodental landmarks (Types I, II, and III, plus curves of semilandmarks) on a single macaque cranium digitally reconstructed with three different surface scanners and a microCT scanner. Nine researchers with varying degrees of osteological and geometric morphometric knowledge landmarked ten iterations of each scan (40 total) to test the effects of scan quality, researcher experience, and landmark type on levels of intra- and interobserver error. Two researchers additionally landmarked ten specimens from seven different macaque species using the same landmark protocol to test the effects of the previously listed variables relative to species-level morphological differences (i.e., observer variance versus real biological variance). Error rates within and among researchers by scan type were calculated to determine whether or not data collected by different individuals or on different digitally rendered crania are consistent enough to be used in a single dataset. Results indicate that scan type does not impact rate of intra- or interobserver error. Interobserver error is far greater than intraobserver error among all individuals, and is similar in variance to that found among different macaque species. Additionally, experience with osteology and morphometrics both positively contribute to precision in multiple landmarking sessions, even where less experienced researchers have been trained in point acquisition. Individual training increases precision (although not necessarily accuracy), and is highly recommended in any situation where multiple researchers will be collecting data for a single project. PMID:29099867

The use of propagation path corrections to improve regional seismic event location in western China

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

Steck, L.K.; Cogbill, A.H.; Velasco, A.A.

1999-03-01

In an effort to improve the ability to locate seismic events in western China using only regional data, the authors have developed empirical propagation path corrections (PPCs) and applied such corrections using both traditional location routines as well as a nonlinear grid search method. Thus far, the authors have concentrated on corrections to observed P arrival times for shallow events using travel-time observations available from the USGS EDRs, the ISC catalogs, their own travel-tim picks from regional data, and data from other catalogs. They relocate events with the algorithm of Bratt and Bache (1988) from a region encompassing China. Formore » individual stations having sufficient data, they produce a map of the regional travel-time residuals from all well-located teleseismic events. From these maps, interpolated PPC surfaces have been constructed using both surface fitting under tension and modified Bayesian kriging. The latter method offers the advantage of providing well-behaved interpolants, but requires that the authors have adequate error estimates associated with the travel-time residuals. To improve error estimates for kriging and event location, they separate measurement error from modeling error. The modeling error is defined as the travel-time variance of a particular model as a function of distance, while the measurement error is defined as the picking error associated with each phase. They estimate measurement errors for arrivals from the EDRs based on roundoff or truncation, and use signal-to-noise for the travel-time picks from the waveform data set.« less

Data Analysis and Its Impact on Predicting Schedule & Cost Risk

DTIC Science & Technology

2006-03-01

variance of the error term by performing a Breusch - Pagan test for constant variance (Neter et al., 1996:239). In order to test the normality of...is constant variance. Using Microsoft Excel®, we calculate a p- 68 value of 0.225678 for the Breusch - Pagan test . We again compare this p-value to...calculate a p-value of 0.121211092 Breusch - Pagan test . We again compare this p-value to an alpha of 0.05 indicating our assumption of constant variance

Performance Analysis of Digital Tracking Loops for Telemetry Ranging Applications

NASA Astrophysics Data System (ADS)

Vilnrotter, V.; Hamkins, J.; Xie, H.; Ashrafi, S.

2015-08-01

In this article, we analyze mathematical models of digital loops used to track the phase and timing of communications and navigation signals. The limits on the accuracy of phase and timing estimates play a critical role in the accuracy achievable in telemetry ranging applications. We describe in detail a practical algorithm to compute the loop parameters for discrete update (DU) and continuous update (CU) loop formulations, and we show that a simple power-series approximation to the DU model is valid over a large range of time-bandwidth product . Several numerical examples compare the estimation error variance of the DU and CU models to each other and to Cramer-Rao lower bounds. Finally, the results are applied to the problem of ranging, by evaluating the performance of a phase-locked loop designed to track a typical ambiguity-resolving pseudonoise (PN) code received and demodulated at the spacecraft on the uplink part of the two-way ranging link, and a data transition tracking loop (DTTL) on the downlink part.

A standardized method for the calibration of thermodynamic data for the prediction of gas chromatographic retention times.

PubMed

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-02-21

A new method for calibrating thermodynamic data to be used in the prediction of analyte retention times is presented. The method allows thermodynamic data collected on one column to be used in making predictions across columns of the same stationary phase but with varying geometries. This calibration is essential as slight variances in the column inner diameter and stationary phase film thickness between columns or as a column ages will adversely affect the accuracy of predictions. The calibration technique uses a Grob standard mixture along with a Nelder-Mead simplex algorithm and a previously developed model of GC retention times based on a three-parameter thermodynamic model to estimate both inner diameter and stationary phase film thickness. The calibration method is highly successful with the predicted retention times for a set of alkanes, ketones and alcohols having an average error of 1.6s across three columns. Copyright © 2014 Elsevier B.V. All rights reserved.

The Expected Sample Variance of Uncorrelated Random Variables with a Common Mean and Some Applications in Unbalanced Random Effects Models

ERIC Educational Resources Information Center

Vardeman, Stephen B.; Wendelberger, Joanne R.

2005-01-01

There is a little-known but very simple generalization of the standard result that for uncorrelated random variables with common mean [mu] and variance [sigma][superscript 2], the expected value of the sample variance is [sigma][superscript 2]. The generalization justifies the use of the usual standard error of the sample mean in possibly…

A model and variance reduction method for computing statistical outputs of stochastic elliptic partial differential equations

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

Vidal-Codina, F., E-mail: fvidal@mit.edu; Nguyen, N.C., E-mail: cuongng@mit.edu; Giles, M.B., E-mail: mike.giles@maths.ox.ac.uk

We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations. Our method consists of three main ingredients: (1) the hybridizable discontinuous Galerkin (HDG) discretization of elliptic partial differential equations (PDEs), which allows us to obtain high-order accurate solutions of the governing PDE; (2) the reduced basis method for a new HDG discretization of the underlying PDE to enable real-time solution of the parameterized PDE in the presence of stochastic parameters; and (3) a multilevel variance reduction method that exploits the statistical correlation among the different reduced basismore » approximations and the high-fidelity HDG discretization to accelerate the convergence of the Monte Carlo simulations. The multilevel variance reduction method provides efficient computation of the statistical outputs by shifting most of the computational burden from the high-fidelity HDG approximation to the reduced basis approximations. Furthermore, we develop a posteriori error estimates for our approximations of the statistical outputs. Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance. We provide numerical examples to demonstrate the performance of the proposed method.« less

A Posteriori Correction of Forecast and Observation Error Variances

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid

2005-01-01

Proposed method of total observation and forecast error variance correction is based on the assumption about normal distribution of "observed-minus-forecast" residuals (O-F), where O is an observed value and F is usually a short-term model forecast. This assumption can be accepted for several types of observations (except humidity) which are not grossly in error. Degree of nearness to normal distribution can be estimated by the symmetry or skewness (luck of symmetry) a(sub 3) = mu(sub 3)/sigma(sup 3) and kurtosis a(sub 4) = mu(sub 4)/sigma(sup 4) - 3 Here mu(sub i) = i-order moment, sigma is a standard deviation. It is well known that for normal distribution a(sub 3) = a(sub 4) = 0.

Growth models and the expected distribution of fluctuating asymmetry

USGS Publications Warehouse

Graham, John H.; Shimizu, Kunio; Emlen, John M.; Freeman, D. Carl; Merkel, John

2003-01-01

Multiplicative error accounts for much of the size-scaling and leptokurtosis in fluctuating asymmetry. It arises when growth involves the addition of tissue to that which is already present. Such errors are lognormally distributed. The distribution of the difference between two lognormal variates is leptokurtic. If those two variates are correlated, then the asymmetry variance will scale with size. Inert tissues typically exhibit additive error and have a gamma distribution. Although their asymmetry variance does not exhibit size-scaling, the distribution of the difference between two gamma variates is nevertheless leptokurtic. Measurement error is also additive, but has a normal distribution. Thus, the measurement of fluctuating asymmetry may involve the mixing of additive and multiplicative error. When errors are multiplicative, we recommend computing log E(l) − log E(r), the difference between the logarithms of the expected values of left and right sides, even when size-scaling is not obvious. If l and r are lognormally distributed, and measurement error is nil, the resulting distribution will be normal, and multiplicative error will not confound size-related changes in asymmetry. When errors are additive, such a transformation to remove size-scaling is unnecessary. Nevertheless, the distribution of l − r may still be leptokurtic.

An improved triple collocation algorithm for decomposing autocorrelated and white soil moisture retrieval errors

USDA-ARS?s Scientific Manuscript database

If not properly account for, auto-correlated errors in observations can lead to inaccurate results in soil moisture data analysis and reanalysis. Here, we propose a more generalized form of the triple collocation algorithm (GTC) capable of decomposing the total error variance of remotely-sensed surf...

Robust LOD scores for variance component-based linkage analysis.

PubMed

Blangero, J; Williams, J T; Almasy, L

2000-01-01

The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, Ronald M.

2015-01-01

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

Phase-locking and coherent power combining of broadband linearly chirped optical waves.

PubMed

Satyan, Naresh; Vasilyev, Arseny; Rakuljic, George; White, Jeffrey O; Yariv, Amnon

2012-11-05

We propose, analyze and demonstrate the optoelectronic phase-locking of optical waves whose frequencies are chirped continuously and rapidly with time. The optical waves are derived from a common optoelectronic swept-frequency laser based on a semiconductor laser in a negative feedback loop, with a precisely linear frequency chirp of 400 GHz in 2 ms. In contrast to monochromatic waves, a differential delay between two linearly chirped optical waves results in a mutual frequency difference, and an acoustooptic frequency shifter is therefore used to phase-lock the two waves. We demonstrate and characterize homodyne and heterodyne optical phase-locked loops with rapidly chirped waves, and show the ability to precisely control the phase of the chirped optical waveform using a digital electronic oscillator. A loop bandwidth of ~ 60 kHz, and a residual phase error variance of < 0.01 rad(2) between the chirped waves is obtained. Further, we demonstrate the simultaneous phase-locking of two optical paths to a common master waveform, and the ability to electronically control the resultant two-element optical phased array. The results of this work enable coherent power combining of high-power fiber amplifiers-where a rapidly chirping seed laser reduces stimulated Brillouin scattering-and electronic beam steering of chirped optical waves.

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration

PubMed Central

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-01-01

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper. PMID:27144570

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration.

PubMed

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-05-02

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.

Impact of nonzero boresight pointing errors on the performance of a relay-assisted free-space optical communication system over exponentiated Weibull fading channels.

PubMed

Wang, Ping; Liu, Xiaoxia; Cao, Tian; Fu, Huihua; Wang, Ranran; Guo, Lixin

2016-09-20

The impact of nonzero boresight pointing errors on the system performance of decode-and-forward protocol-based multihop parallel optical wireless communication systems is studied. For the aggregated fading channel, the atmospheric turbulence is simulated by an exponentiated Weibull model, and pointing errors are described by one recently proposed statistical model including both boresight and jitter. The binary phase-shift keying subcarrier intensity modulation-based analytical average bit error rate (ABER) and outage probability expressions are achieved for a nonidentically and independently distributed system. The ABER and outage probability are then analyzed with different turbulence strengths, receiving aperture sizes, structure parameters (P and Q), jitter variances, and boresight displacements. The results show that aperture averaging offers almost the same system performance improvement with boresight included or not, despite the values of P and Q. The performance enhancement owing to the increase of cooperative path (P) is more evident with nonzero boresight than that with zero boresight (jitter only), whereas the performance deterioration because of the increasing hops (Q) with nonzero boresight is almost the same as that with zero boresight. Monte Carlo simulation is offered to verify the validity of ABER and outage probability expressions.

Analytic score distributions for a spatially continuous tridirectional Monte Carol transport problem

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

Booth, T.E.

1996-01-01

The interpretation of the statistical error estimates produced by Monte Carlo transport codes is still somewhat of an art. Empirically, there are variance reduction techniques whose error estimates are almost always reliable, and there are variance reduction techniques whose error estimates are often unreliable. Unreliable error estimates usually result from inadequate large-score sampling from the score distribution`s tail. Statisticians believe that more accurate confidence interval statements are possible if the general nature of the score distribution can be characterized. Here, the analytic score distribution for the exponential transform applied to a simple, spatially continuous Monte Carlo transport problem is provided.more » Anisotropic scattering and implicit capture are included in the theory. In large part, the analytic score distributions that are derived provide the basis for the ten new statistical quality checks in MCNP.« less

Options to Improve Rain Snow Parameterization in Surface Based Models

NASA Astrophysics Data System (ADS)

Feiccabrino, J. M.

2017-12-01

Precipitation phase determination is of upmost importance in a number of surface based hydrological, ecological, and safety models. However, precipitation phase at Earth's surface is a result of cloud and atmospheric properties not measured by surface weather stations. Nonetheless, they can be inferred from the available surface datum. This study uses 681,620 weather observations with air temperatures between -3 and 5°C and identified precipitation occurring at the time of the observation to determine simple, yet accurate, thresholds for precipitation phase determination schemes (PPDS). This dataset represents 38% and 42% of precipitation observations over a 16 year period for 85 Swedish, and 84 Norwegian weather stations. The misclassified precipitation (error) from PPDS using AT, dew-point temperature (DT) and wet-bulb temperature (WB) thresholds were compared using a single threshold PPDS. The Norwegian observations between -3 and 5°C resulted in 11.64%, 11.21%, and 8.42% error for DT (-0.2°C), AT (1.2°C), and WB (0.3°C) thresholds respectively. Individual station thresholds had a range of -0.7 to 1.2°C, -1.2 to 0.9°C, and -0.1 to 2.5°C for WB, DP, and AT respectively. To address threshold variance while decreasing error, weather stations were grouped into nine landscape categories; windward (WW) ocean, WW coast, WW fjord, WW hill, WW mountain, leeward (LW) mountain, LW hill, LW rolling hills, and LW coast. Landscape classification was based on location relative to the Scandinavian Mountains, and the % water or range of elevation within 15KM. Within landscapes, stations share similar land atmosphere exchanges which differ from other landscapes. These differences change optimal thresholds for PPDS between landscapes. Also tested were threshold temperature affects based on assumed atmospheric differences for the following observation groups; 1.) occurring before and after an air mass boundary, 2.) with different water temperatures and/or NAO phases, 3.) with snow cover, 4.) coupled with higher elevation stations and 5.) with different cloud heights. For example, in Norway, as the unsaturated layer depth beneath clouds increased, AT thresholds warmed. Cloud height adjusted AT thresholds reduced error by 5% before threshold adjustments for landscapes.

Statistics of the epoch of reionization 21-cm signal - I. Power spectrum error-covariance

NASA Astrophysics Data System (ADS)

Mondal, Rajesh; Bharadwaj, Somnath; Majumdar, Suman

2016-02-01

The non-Gaussian nature of the epoch of reionization (EoR) 21-cm signal has a significant impact on the error variance of its power spectrum P(k). We have used a large ensemble of seminumerical simulations and an analytical model to estimate the effect of this non-Gaussianity on the entire error-covariance matrix {C}ij. Our analytical model shows that {C}ij has contributions from two sources. One is the usual variance for a Gaussian random field which scales inversely of the number of modes that goes into the estimation of P(k). The other is the trispectrum of the signal. Using the simulated 21-cm Signal Ensemble, an ensemble of the Randomized Signal and Ensembles of Gaussian Random Ensembles we have quantified the effect of the trispectrum on the error variance {C}II. We find that its relative contribution is comparable to or larger than that of the Gaussian term for the k range 0.3 ≤ k ≤ 1.0 Mpc-1, and can be even ˜200 times larger at k ˜ 5 Mpc-1. We also establish that the off-diagonal terms of {C}ij have statistically significant non-zero values which arise purely from the trispectrum. This further signifies that the error in different k modes are not independent. We find a strong correlation between the errors at large k values (≥0.5 Mpc-1), and a weak correlation between the smallest and largest k values. There is also a small anticorrelation between the errors in the smallest and intermediate k values. These results are relevant for the k range that will be probed by the current and upcoming EoR 21-cm experiments.

The Statistical Power of Planned Comparisons.

ERIC Educational Resources Information Center

Benton, Roberta L.

Basic principles underlying statistical power are examined; and issues pertaining to effect size, sample size, error variance, and significance level are highlighted via the use of specific hypothetical examples. Analysis of variance (ANOVA) and related methods remain popular, although other procedures sometimes have more statistical power against…

On the error in crop acreage estimation using satellite (LANDSAT) data

NASA Technical Reports Server (NTRS)

Chhikara, R. (Principal Investigator)

1983-01-01

The problem of crop acreage estimation using satellite data is discussed. Bias and variance of a crop proportion estimate in an area segment obtained from the classification of its multispectral sensor data are derived as functions of the means, variances, and covariance of error rates. The linear discriminant analysis and the class proportion estimation for the two class case are extended to include a third class of measurement units, where these units are mixed on ground. Special attention is given to the investigation of mislabeling in training samples and its effect on crop proportion estimation. It is shown that the bias and variance of the estimate of a specific crop acreage proportion increase as the disparity in mislabeling rates between two classes increases. Some interaction is shown to take place, causing the bias and the variance to decrease at first and then to increase, as the mixed unit class varies in size from 0 to 50 percent of the total area segment.

Lognormal Kalman filter for assimilating phase space density data in the radiation belts

NASA Astrophysics Data System (ADS)

Kondrashov, D.; Ghil, M.; Shprits, Y.

2011-11-01

Data assimilation combines a physical model with sparse observations and has become an increasingly important tool for scientists and engineers in the design, operation, and use of satellites and other high-technology systems in the near-Earth space environment. Of particular importance is predicting fluxes of high-energy particles in the Van Allen radiation belts, since these fluxes can damage spaceborne platforms and instruments during strong geomagnetic storms. In transiting from a research setting to operational prediction of these fluxes, improved data assimilation is of the essence. The present study is motivated by the fact that phase space densities (PSDs) of high-energy electrons in the outer radiation belt—both simulated and observed—are subject to spatiotemporal variations that span several orders of magnitude. Standard data assimilation methods that are based on least squares minimization of normally distributed errors may not be adequate for handling the range of these variations. We propose herein a modification of Kalman filtering that uses a log-transformed, one-dimensional radial diffusion model for the PSDs and includes parameterized losses. The proposed methodology is first verified on model-simulated, synthetic data and then applied to actual satellite measurements. When the model errors are sufficiently smaller then observational errors, our methodology can significantly improve analysis and prediction skill for the PSDs compared to those of the standard Kalman filter formulation. This improvement is documented by monitoring the variance of the innovation sequence.

Linear score tests for variance components in linear mixed models and applications to genetic association studies.

PubMed

Qu, Long; Guennel, Tobias; Marshall, Scott L

2013-12-01

Following the rapid development of genome-scale genotyping technologies, genetic association mapping has become a popular tool to detect genomic regions responsible for certain (disease) phenotypes, especially in early-phase pharmacogenomic studies with limited sample size. In response to such applications, a good association test needs to be (1) applicable to a wide range of possible genetic models, including, but not limited to, the presence of gene-by-environment or gene-by-gene interactions and non-linearity of a group of marker effects, (2) accurate in small samples, fast to compute on the genomic scale, and amenable to large scale multiple testing corrections, and (3) reasonably powerful to locate causal genomic regions. The kernel machine method represented in linear mixed models provides a viable solution by transforming the problem into testing the nullity of variance components. In this study, we consider score-based tests by choosing a statistic linear in the score function. When the model under the null hypothesis has only one error variance parameter, our test is exact in finite samples. When the null model has more than one variance parameter, we develop a new moment-based approximation that performs well in simulations. Through simulations and analysis of real data, we demonstrate that the new test possesses most of the aforementioned characteristics, especially when compared to existing quadratic score tests or restricted likelihood ratio tests. © 2013, The International Biometric Society.

Power Measurement Errors on a Utility Aircraft

NASA Technical Reports Server (NTRS)

Bousman, William G.

2002-01-01

Extensive flight test data obtained from two recent performance tests of a UH 60A aircraft are reviewed. A power difference is calculated from the power balance equation and is used to examine power measurement errors. It is shown that the baseline measurement errors are highly non-Gaussian in their frequency distribution and are therefore influenced by additional, unquantified variables. Linear regression is used to examine the influence of other variables and it is shown that a substantial portion of the variance depends upon measurements of atmospheric parameters. Correcting for temperature dependence, although reducing the variance in the measurement errors, still leaves unquantified effects. Examination of the power difference over individual test runs indicates significant errors from drift, although it is unclear how these may be corrected. In an idealized case, where the drift is correctable, it is shown that the power measurement errors are significantly reduced and the error distribution is Gaussian. A new flight test program is recommended that will quantify the thermal environment for all torque measurements on the UH 60. Subsequently, the torque measurement systems will be recalibrated based on the measured thermal environment and a new power measurement assessment performed.

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes.

PubMed

Johnson, Jacqueline L; Kreidler, Sarah M; Catellier, Diane J; Murray, David M; Muller, Keith E; Glueck, Deborah H

2015-11-30

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach. Copyright © 2015 John Wiley & Sons, Ltd.

How many drinks did you have on September 11, 2001?

PubMed

Perrine, M W Bud; Schroder, Kerstin E E

2005-07-01

This study tested the predictability of error in retrospective self-reports of alcohol consumption on September 11, 2001, among 80 Vermont light, medium and heavy drinkers. Subjects were 52 men and 28 women participating in daily self-reports of alcohol consumption for a total of 2 years, collected via interactive voice response technology (IVR). In addition, retrospective self-reports of alcohol consumption on September 11, 2001, were collected by telephone interview 4-5 days following the terrorist attacks. Retrospective error was calculated as the difference between the IVR self-report of drinking behavior on September 11 and the retrospective self-report collected by telephone interview. Retrospective error was analyzed as a function of gender and baseline drinking behavior during the 365 days preceding September 11, 2001 (termed "the baseline"). The intraclass correlation (ICC) between daily IVR and retrospective self-reports of alcohol consumption on September 11 was .80. Women provided, on average, more accurate self-reports (ICC = .96) than men (ICC = .72) but displayed more underreporting bias in retrospective responses. Amount and individual variability of alcohol consumption during the 1-year baseline explained, on average, 11% of the variance in overreporting (r = .33), 9% of the variance in underreporting (r = .30) and 25% of the variance in the overall magnitude of error (r = .50), with correlations up to .62 (r2 = .38). The size and direction of error were clearly predictable from the amount and variation in drinking behavior during the 1-year baseline period. The results demonstrate the utility and detail of information that can be derived from daily IVR self-reports in the analysis of retrospective error.

Error in geometric morphometric data collection: Combining data from multiple sources.

PubMed

Robinson, Chris; Terhune, Claire E

2017-09-01

This study compares two- and three-dimensional morphometric data to determine the extent to which intra- and interobserver and intermethod error influence the outcomes of statistical analyses. Data were collected five times for each method and observer on 14 anthropoid crania using calipers, a MicroScribe, and 3D models created from NextEngine and microCT scans. ANOVA models were used to examine variance in the linear data at the level of genus, species, specimen, observer, method, and trial. Three-dimensional data were analyzed using geometric morphometric methods; principal components analysis was employed to examine how trials of all specimens were distributed in morphospace and Procrustes distances among trials were calculated and used to generate UPGMA trees to explore whether all trials of the same individual grouped together regardless of observer or method. Most variance in the linear data was at the genus level, with greater variance at the observer than method levels. In the 3D data, interobserver and intermethod error were similar to intraspecific distances among Callicebus cupreus individuals, with interobserver error being higher than intermethod error. Generally, taxa separate well in morphospace, with different trials of the same specimen typically grouping together. However, trials of individuals in the same species overlapped substantially with one another. Researchers should be cautious when compiling data from multiple methods and/or observers, especially if analyses are focused on intraspecific variation or closely related species, as in these cases, patterns among individuals may be obscured by interobserver and intermethod error. Conducting interobserver and intermethod reliability assessments prior to the collection of data is recommended. © 2017 Wiley Periodicals, Inc.

Designing Measurement Studies under Budget Constraints: Controlling Error of Measurement and Power.

ERIC Educational Resources Information Center

Marcoulides, George A.

1995-01-01

A methodology is presented for minimizing the mean error variance-covariance component in studies with resource constraints. The method is illustrated using a one-facet multivariate design. Extensions to other designs are discussed. (SLD)

Peptidic β-sheet binding with Congo Red allows both reduction of error variance and signal amplification for immunoassays.

PubMed

Wang, Yunyun; Liu, Ye; Deng, Xinli; Cong, Yulong; Jiang, Xingyu

2016-12-15

Although conventional enzyme-linked immunosorbent assays (ELISA) and related assays have been widely applied for the diagnosis of diseases, many of them suffer from large error variance for monitoring the concentration of targets over time, and insufficient limit of detection (LOD) for assaying dilute targets. We herein report a readout mode of ELISA based on the binding between peptidic β-sheet structure and Congo Red. The formation of peptidic β-sheet structure is triggered by alkaline phosphatase (ALP). For the detection of P-Selectin which is a crucial indicator for evaluating thrombus diseases in clinic, the 'β-sheet and Congo Red' mode significantly decreases both the error variance and the LOD (from 9.7ng/ml to 1.1 ng/ml) of detection, compared with commercial ELISA (an existing gold-standard method for detecting P-Selectin in clinic). Considering the wide range of ALP-based antibodies for immunoassays, such novel method could be applicable to the analysis of many types of targets. Copyright © 2016 Elsevier B.V. All rights reserved.

Artifact-resistant superimposition of digital dental models and cone-beam computed tomography images.

PubMed

Lin, Hsiu-Hsia; Chiang, Wen-Chung; Lo, Lun-Jou; Sheng-Pin Hsu, Sam; Wang, Chien-Hsuan; Wan, Shu-Yen

2013-11-01

Combining the maxillofacial cone-beam computed tomography (CBCT) model with its corresponding digital dental model enables an integrated 3-dimensional (3D) representation of skeletal structures, teeth, and occlusions. Undesired artifacts, however, introduce difficulties in the superimposition of both models. We have proposed an artifact-resistant surface-based registration method that is robust and clinically applicable and that does not require markers. A CBCT bone model and a laser-scanned dental model obtained from the same patient were used in developing the method and examining the accuracy of the superimposition. Our method included 4 phases. The first phase was to segment the maxilla from the mandible in the CBCT model. The second phase was to conduct an initial registration to bring the digital dental model and the maxilla and mandible sufficiently close to each other. Third, we manually selected at least 3 corresponding regions on both models by smearing patches on the 3D surfaces. The last phase was to superimpose the digital dental model into the maxillofacial model. Each superimposition process was performed twice by 2 operators with the same object to investigate the intra- and interoperator differences. All collected objects were divided into 3 groups with various degrees of artifacts: artifact-free, critical artifacts, and severe artifacts. The mean errors and root-mean-square (RMS) errors were used to evaluate the accuracy of the superimposition results. Repeated measures analysis of variance and the Wilcoxon rank sum test were used to calculate the intraoperator reproducibility and interoperator reliability. Twenty-four maxilla and mandible objects for evaluation were obtained from 14 patients. The experimental results showed that the mean errors between the 2 original models in the residing fused model ranged from 0.10 to 0.43 mm and that the RMS errors ranged from 0.13 to 0.53 mm. These data were consistent with previously used methods and were clinically acceptable. All measurements of the proposed study exhibited desirable intraoperator reproducibility and interoperator reliability. Regarding the intra- and interoperator mean errors and RMS errors in the nonartifact or critical artifact group, no significant difference between the repeated trials or between operators (P < .05) was observed. The results of the present study have shown that the proposed regional surface-based registration can robustly and accurately superimpose a digital dental model into its corresponding CBCT model. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

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...

Taking the Error Term of the Factor Model into Account: The Factor Score Predictor Interval

ERIC Educational Resources Information Center

Beauducel, Andre

2013-01-01

The problem of factor score indeterminacy implies that the factor and the error scores cannot be completely disentangled in the factor model. It is therefore proposed to compute Harman's factor score predictor that contains an additive combination of factor and error variance. This additive combination is discussed in the framework of classical…

On the Fallibility of Principal Components in Research

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

2017-01-01

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…

Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

NASA Astrophysics Data System (ADS)

Solazzo, Efisio; Hogrefe, Christian; Colette, Augustin; Garcia-Vivanco, Marta; Galmarini, Stefano

2017-09-01

The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ˜ 1.5 days account for 70-85 % of the mean square error of the full (undecomposed) ozone time series; (ii) a recursive, systematic error with daily periodicity is detected, responsible for 10-20 % of the quadratic total error; (iii) errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); (iv) the CMAQ ozone error has a weak/negligible dependence on the errors in NO2, while the error in NO2 significantly impacts the ozone error produced by Chimere; (v) the response of the models to variations of anthropogenic emissions and boundary conditions show a pronounced spatial heterogeneity, while the seasonal variability of the response is found to be less marked. Only during the winter season does the zeroing of boundary values for North America produce a spatially uniform deterioration of the model accuracy across the majority of the continent.

Research on effects of phase error in phase-shifting interferometer

NASA Astrophysics Data System (ADS)

Wang, Hongjun; Wang, Zhao; Zhao, Hong; Tian, Ailing; Liu, Bingcai

2007-12-01

Referring to phase-shifting interferometry technology, the phase shifting error from the phase shifter is the main factor that directly affects the measurement accuracy of the phase shifting interferometer. In this paper, the resources and sorts of phase shifting error were introduction, and some methods to eliminate errors were mentioned. Based on the theory of phase shifting interferometry, the effects of phase shifting error were analyzed in detail. The Liquid Crystal Display (LCD) as a new shifter has advantage as that the phase shifting can be controlled digitally without any mechanical moving and rotating element. By changing coded image displayed on LCD, the phase shifting in measuring system was induced. LCD's phase modulation characteristic was analyzed in theory and tested. Based on Fourier transform, the effect model of phase error coming from LCD was established in four-step phase shifting interferometry. And the error range was obtained. In order to reduce error, a new error compensation algorithm was put forward. With this method, the error can be obtained by process interferogram. The interferogram can be compensated, and the measurement results can be obtained by four-step phase shifting interferogram. Theoretical analysis and simulation results demonstrate the feasibility of this approach to improve measurement accuracy.

Importance of Geosat orbit and tidal errors in the estimation of large-scale Indian Ocean variations

NASA Technical Reports Server (NTRS)

Perigaud, Claire; Zlotnicki, Victor

1992-01-01

To improve the estimate accuracy of large-scale meridional sea-level variations, Geosat ERM data on the Indian Ocean for a 26-month period were processed using two different techniques of orbit error reduction. The first technique removes an along-track polynomial of degree 1 over about 5000 km and the second technique removes an along-track once-per-revolution sine wave about 40,000 km. Results obtained show that the polynomial technique produces stronger attenuation of both the tidal error and the large-scale oceanic signal. After filtering, the residual difference between the two methods represents 44 percent of the total variance and 23 percent of the annual variance. The sine-wave method yields a larger estimate of annual and interannual meridional variations.

Comparative test on several forms of background error covariance in 3DVar

NASA Astrophysics Data System (ADS)

Shao, Aimei

2013-04-01

The background error covariance matrix (Hereinafter referred to as B matrix) plays an important role in the three-dimensional variational (3DVar) data assimilation method. However, it is difficult to get B matrix accurately because true atmospheric state is unknown. Therefore, some methods were developed to estimate B matrix (e.g. NMC method, innovation analysis method, recursive filters, and ensemble method such as EnKF). Prior to further development and application of these methods, the function of several B matrixes estimated by these methods in 3Dvar is worth studying and evaluating. For this reason, NCEP reanalysis data and forecast data are used to test the effectiveness of the several B matrixes with VAF (Huang, 1999) method. Here the NCEP analysis is treated as the truth and in this case the forecast error is known. The data from 2006 to 2007 is used as the samples to estimate B matrix and the data in 2008 is used to verify the assimilation effects. The 48h and 24h forecast valid at the same time is used to estimate B matrix with NMC method. B matrix can be represented by a correlation part (a non-diagonal matrix) and a variance part (a diagonal matrix of variances). Gaussian filter function as an approximate approach is used to represent the variation of correlation coefficients with distance in numerous 3DVar systems. On the basis of the assumption, the following several forms of B matrixes are designed and test with VAF in the comparative experiments: (1) error variance and the characteristic lengths are fixed and setted to their mean value averaged over the analysis domain; (2) similar to (1), but the mean characteristic lengths reduce to 50 percent for the height and 60 percent for the temperature of the original; (3) similar to (2), but error variance calculated directly by the historical data is space-dependent; (4) error variance and characteristic lengths are all calculated directly by the historical data; (5) B matrix is estimated directly by the historical data; (6) similar to (5), but a localization process is performed; (7) B matrix is estimated by NMC method but error variance is reduced by 1.7 times in order that the value is close to that calculated from the true forecast error samples; (8) similar to (7), but the localization similar to (6) is performed. Experimental results with the different B matrixes show that for the Gaussian-type B matrix the characteristic lengths calculated from the true error samples don't bring a good analysis results. However, the reduced characteristic lengths (about half of the original one) can lead to a good analysis. If the B matrix estimated directly from the historical data is used in 3DVar, the assimilation effect can not reach to the best. The better assimilation results are generated with the application of reduced characteristic length and localization. Even so, it hasn't obvious advantage compared with Gaussian-type B matrix with the optimal characteristic length. It implies that the Gaussian-type B matrix, widely used for operational 3DVar system, can get a good analysis with the appropriate characteristic lengths. The crucial problem is how to determine the appropriate characteristic lengths. (This work is supported by the National Natural Science Foundation of China (41275102, 40875063), and the Fundamental Research Funds for the Central Universities (lzujbky-2010-9) )

Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations

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

Kleijnen, J.P.C.; Helton, J.C.

1999-04-01

The robustness of procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses is investigated. These procedures are based on attempts to detect increasingly complex patterns in the scatterplots under consideration and involve the identification of (1) linear relationships with correlation coefficients, (2) monotonic relationships with rank correlation coefficients, (3) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (4) trends in variability as defined by variances and interquartile ranges, and (5) deviations from randomness as defined by the chi-square statistic. The following two topics related to the robustness of these procedures are consideredmore » for a sequence of example analyses with a large model for two-phase fluid flow: the presence of Type I and Type II errors, and the stability of results obtained with independent Latin hypercube samples. Observations from analysis include: (1) Type I errors are unavoidable, (2) Type II errors can occur when inappropriate analysis procedures are used, (3) physical explanations should always be sought for why statistical procedures identify variables as being important, and (4) the identification of important variables tends to be stable for independent Latin hypercube samples.« less

Jackknife Estimation of Sampling Variance of Ratio Estimators in Complex Samples: Bias and the Coefficient of Variation. Research Report. ETS RR-06-19

ERIC Educational Resources Information Center

Oranje, Andreas

2006-01-01

A multitude of methods has been proposed to estimate the sampling variance of ratio estimates in complex samples (Wolter, 1985). Hansen and Tepping (1985) studied some of those variance estimators and found that a high coefficient of variation (CV) of the denominator of a ratio estimate is indicative of a biased estimate of the standard error of a…

Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection.

PubMed

He, Jianbo; Li, Jijie; Huang, Zhongwen; Zhao, Tuanjie; Xing, Guangnan; Gai, Junyi; Guan, Rongzhan

2015-01-01

Experimental error control is very important in quantitative trait locus (QTL) mapping. Although numerous statistical methods have been developed for QTL mapping, a QTL detection model based on an appropriate experimental design that emphasizes error control has not been developed. Lattice design is very suitable for experiments with large sample sizes, which is usually required for accurate mapping of quantitative traits. However, the lack of a QTL mapping method based on lattice design dictates that the arithmetic mean or adjusted mean of each line of observations in the lattice design had to be used as a response variable, resulting in low QTL detection power. As an improvement, we developed a QTL mapping method termed composite interval mapping based on lattice design (CIMLD). In the lattice design, experimental errors are decomposed into random errors and block-within-replication errors. Four levels of block-within-replication errors were simulated to show the power of QTL detection under different error controls. The simulation results showed that the arithmetic mean method, which is equivalent to a method under random complete block design (RCBD), was very sensitive to the size of the block variance and with the increase of block variance, the power of QTL detection decreased from 51.3% to 9.4%. In contrast to the RCBD method, the power of CIMLD and the adjusted mean method did not change for different block variances. The CIMLD method showed 1.2- to 7.6-fold higher power of QTL detection than the arithmetic or adjusted mean methods. Our proposed method was applied to real soybean (Glycine max) data as an example and 10 QTLs for biomass were identified that explained 65.87% of the phenotypic variation, while only three and two QTLs were identified by arithmetic and adjusted mean methods, respectively.

Low-dimensional Representation of Error Covariance

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan

2000-01-01

Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

Downscaling large-scale circulation to local winter climate using neural network techniques

NASA Astrophysics Data System (ADS)

Cavazos Perez, Maria Tereza

1998-12-01

The severe impacts of climate variability on society reveal the increasing need for improving regional-scale climate diagnosis. A new downscaling approach for climate diagnosis is developed here. It is based on neural network techniques that derive transfer functions from the large-scale atmospheric controls to the local winter climate in northeastern Mexico and southeastern Texas during the 1985-93 period. A first neural network (NN) model employs time-lagged component scores from a rotated principal component analysis of SLP, 500-hPa heights, and 1000-500 hPa thickness as predictors of daily precipitation. The model is able to reproduce the phase and, to some decree, the amplitude of large rainfall events, reflecting the influence of the large-scale circulation. Large errors are found over the Sierra Madre, over the Gulf of Mexico, and during El Nino events, suggesting an increase in the importance of meso-scale rainfall processes. However, errors are also due to the lack of randomization of the input data and the absence of local atmospheric predictors such as moisture. Thus, a second NN model uses time-lagged specific humidity at the Earth's surface and at the 700 hPa level, SLP tendency, and 700-500 hPa thickness as input to a self-organizing map (SOM) that pre-classifies the atmospheric fields into different patterns. The results from the SOM classification document that negative (positive) anomalies of winter precipitation over the region are associated with: (1) weaker (stronger) Aleutian low; (2) stronger (weaker) North Pacific high; (3) negative (positive) phase of the Pacific North American pattern; and (4) La Nina (El Nino) events. The SOM atmospheric patterns are then used as input to a feed-forward NN that captures over 60% of the daily rainfall variance and 94% of the daily minimum temperature variance over the region. This demonstrates the ability of artificial neural network models to simulate realistic relationships on daily time scales. The results of this research also reveal that the SOM pre-classification of days with similar atmospheric conditions succeeded in emphasizing the differences of the atmospheric variance conducive to extreme events. This resulted in a downscaling NN model that is highly sensitive to local-scale weather anomalies associated with El Nino and extreme cold events.

Errors in the estimation of the variance: implications for multiple-probability fluctuation analysis.

PubMed

Saviane, Chiara; Silver, R Angus

2006-06-15

Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.

Repeatability and reproducibility of ribotyping and its computer interpretation.

PubMed

Lefresne, Gwénola; Latrille, Eric; Irlinger, Françoise; Grimont, Patrick A D

2004-04-01

Many molecular typing methods are difficult to interpret because their repeatability (within-laboratory variance) and reproducibility (between-laboratory variance) have not been thoroughly studied. In the present work, ribotyping of coryneform bacteria was the basis of a study involving within-gel and between-gel repeatability and between-laboratory reproducibility (two laboratories involved). The effect of different technical protocols, different algorithms, and different software for fragment size determination was studied. Analysis of variance (ANOVA) showed, within a laboratory, that there was no significant added variance between gels. However, between-laboratory variance was significantly higher than within-laboratory variance. This may be due to the use of different protocols. An experimental function was calculated to transform the data and make them compatible (i.e., erase the between-laboratory variance). The use of different interpolation algorithms (spline, Schaffer and Sederoff) was a significant source of variation in one laboratory only. The use of either Taxotron (Institut Pasteur) or GelCompar (Applied Maths) was not a significant source of added variation when the same algorithm (spline) was used. However, the use of Bio-Gene (Vilber Lourmat) dramatically increased the error (within laboratory, within gel) in one laboratory, while decreasing the error in the other laboratory; this might be due to automatic normalization attempts. These results were taken into account for building a database and performing automatic pattern identification using Taxotron. Conversion of the data considerably improved the identification of patterns irrespective of the laboratory in which the data were obtained.

Evaluation of assumptions in soil moisture triple collocation analysis

USDA-ARS?s Scientific Manuscript database

Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually-independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and ort...

The Importance of Relying on the Manual: Scoring Error Variance in the WISC-IV Vocabulary Subtest

ERIC Educational Resources Information Center

Erdodi, Laszlo A.; Richard, David C. S.; Hopwood, Christopher

2009-01-01

Classical test theory assumes that ability level has no effect on measurement error. Newer test theories, however, argue that the precision of a measurement instrument changes as a function of the examinee's true score. Research has shown that administration errors are common in the Wechsler scales and that subtests requiring subjective scoring…

Integrating mean and variance heterogeneities to identify differentially expressed genes.

PubMed

Ouyang, Weiwei; An, Qiang; Zhao, Jinying; Qin, Huaizhen

2016-12-06

In functional genomics studies, tests on mean heterogeneity have been widely employed to identify differentially expressed genes with distinct mean expression levels under different experimental conditions. Variance heterogeneity (aka, the difference between condition-specific variances) of gene expression levels is simply neglected or calibrated for as an impediment. The mean heterogeneity in the expression level of a gene reflects one aspect of its distribution alteration; and variance heterogeneity induced by condition change may reflect another aspect. Change in condition may alter both mean and some higher-order characteristics of the distributions of expression levels of susceptible genes. In this report, we put forth a conception of mean-variance differentially expressed (MVDE) genes, whose expression means and variances are sensitive to the change in experimental condition. We mathematically proved the null independence of existent mean heterogeneity tests and variance heterogeneity tests. Based on the independence, we proposed an integrative mean-variance test (IMVT) to combine gene-wise mean heterogeneity and variance heterogeneity induced by condition change. The IMVT outperformed its competitors under comprehensive simulations of normality and Laplace settings. For moderate samples, the IMVT well controlled type I error rates, and so did existent mean heterogeneity test (i.e., the Welch t test (WT), the moderated Welch t test (MWT)) and the procedure of separate tests on mean and variance heterogeneities (SMVT), but the likelihood ratio test (LRT) severely inflated type I error rates. In presence of variance heterogeneity, the IMVT appeared noticeably more powerful than all the valid mean heterogeneity tests. Application to the gene profiles of peripheral circulating B raised solid evidence of informative variance heterogeneity. After adjusting for background data structure, the IMVT replicated previous discoveries and identified novel experiment-wide significant MVDE genes. Our results indicate tremendous potential gain of integrating informative variance heterogeneity after adjusting for global confounders and background data structure. The proposed informative integration test better summarizes the impacts of condition change on expression distributions of susceptible genes than do the existent competitors. Therefore, particular attention should be paid to explicitly exploit the variance heterogeneity induced by condition change in functional genomics analysis.

Allan deviation computations of a linear frequency synthesizer system using frequency domain techniques

NASA Technical Reports Server (NTRS)

Wu, Andy

1995-01-01

Allan Deviation computations of linear frequency synthesizer systems have been reported previously using real-time simulations. Even though it takes less time compared with the actual measurement, it is still very time consuming to compute the Allan Deviation for long sample times with the desired confidence level. Also noises, such as flicker phase noise and flicker frequency noise, can not be simulated precisely. The use of frequency domain techniques can overcome these drawbacks. In this paper the system error model of a fictitious linear frequency synthesizer is developed and its performance using a Cesium (Cs) atomic frequency standard (AFS) as a reference is evaluated using frequency domain techniques. For a linear timing system, the power spectral density at the system output can be computed with known system transfer functions and known power spectral densities from the input noise sources. The resulting power spectral density can then be used to compute the Allan Variance at the system output. Sensitivities of the Allan Variance at the system output to each of its independent input noises are obtained, and they are valuable for design trade-off and trouble-shooting.

Improved tactile resonance sensor for robotic assisted surgery

NASA Astrophysics Data System (ADS)

Oliva Uribe, David; Schoukens, Johan; Stroop, Ralf

2018-01-01

This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements.

Comparison of athlete-coach perceptions of internal and external load markers for elite junior tennis training.

PubMed

Murphy, Alistair P; Duffield, Rob; Kellett, Aaron; Reid, Machar

2014-09-01

To investigate the discrepancy between coach and athlete perceptions of internal load and notational analysis of external load in elite junior tennis. Fourteen elite junior tennis players and 6 international coaches were recruited. Ratings of perceived exertion (RPEs) were recorded for individual drills and whole sessions, along with a rating of mental exertion, coach rating of intended session exertion, and athlete heart rate (HR). Furthermore, total stroke count and unforced-error count were notated using video coding after each session, alongside coach and athlete estimations of shots and errors made. Finally, regression analyses explained the variance in the criterion variables of athlete and coach RPE. Repeated-measures analyses of variance and interclass correlation coefficients revealed that coaches significantly (P < .01) underestimated athlete session RPE, with only moderate correlation (r = .59) demonstrated between coach and athlete. However, athlete drill RPE (P = .14; r = .71) and mental exertion (P = .44; r = .68) were comparable and substantially correlated. No significant differences in estimated stroke count were evident between athlete and coach (P = .21), athlete notational analysis (P = .06), or coach notational analysis (P = .49). Coaches estimated significantly greater unforced errors than either athletes or notational analysis (P < .01). Regression analyses found that 54.5% of variance in coach RPE was explained by intended session exertion and coach drill RPE, while drill RPE and peak HR explained 45.3% of the variance in athlete session RPE. Coaches misinterpreted session RPE but not drill RPE, while inaccurately monitoring error counts. Improved understanding of external- and internal-load monitoring may help coach-athlete relationships in individual sports like tennis avoid maladaptive training.

The effect of toe marker placement error on joint kinematics and muscle forces using OpenSim gait simulation.

PubMed

Xu, Hang; Merryweather, Andrew; Bloswick, Donald; Mao, Qi; Wang, Tong

2015-01-01

Marker placement can be a significant source of error in biomechanical studies of human movement. The toe marker placement error is amplified by footwear since the toe marker placement on the shoe only relies on an approximation of underlying anatomical landmarks. Three total knee replacement subjects were recruited and three self-speed gait trials per subject were collected. The height variation between toe and heel markers of four types of footwear was evaluated from the results of joint kinematics and muscle forces using OpenSim. The reference condition was considered as the same vertical height of toe and heel markers. The results showed that the residual variances for joint kinematics had an approximately linear relationship with toe marker placement error for lower limb joints. Ankle dorsiflexion/plantarflexion is most sensitive to toe marker placement error. The influence of toe marker placement error is generally larger for hip flexion/extension and rotation than hip abduction/adduction and knee flexion/extension. The muscle forces responded to the residual variance of joint kinematics to various degrees based on the muscle function for specific joint kinematics. This study demonstrates the importance of evaluating marker error for joint kinematics and muscle forces when explaining relative clinical gait analysis and treatment intervention.

Linear error analysis of slope-area discharge determinations

USGS Publications Warehouse

Kirby, W.H.

1987-01-01

The slope-area method can be used to calculate peak flood discharges when current-meter measurements are not possible. This calculation depends on several quantities, such as water-surface fall, that are subject to large measurement errors. Other critical quantities, such as Manning's n, are not even amenable to direct measurement but can only be estimated. Finally, scour and fill may cause gross discrepancies between the observed condition of the channel and the hydraulic conditions during the flood peak. The effects of these potential errors on the accuracy of the computed discharge have been estimated by statistical error analysis using a Taylor-series approximation of the discharge formula and the well-known formula for the variance of a sum of correlated random variates. The resultant error variance of the computed discharge is a weighted sum of covariances of the various observational errors. The weights depend on the hydraulic and geometric configuration of the channel. The mathematical analysis confirms the rule of thumb that relative errors in computed discharge increase rapidly when velocity heads exceed the water-surface fall, when the flow field is expanding and when lateral velocity variation (alpha) is large. It also confirms the extreme importance of accurately assessing the presence of scour or fill. ?? 1987.

Sources of variation in an enzyme-linked immunoassay of bluetongue virus in Culicoides variipennis (Diptera: Ceratopogonidae).

PubMed

Tabachnick, W J; Mecham, J O

1991-03-01

An enzyme-linked immunoassay for detecting bluetongue virus in infected Culicoides variipennis was evaluated using a nested analysis of variance to determine sources of experimental error in the procedure. The major source of variation was differences among individual insects (84% of the total variance). Storing insects at -70 degrees C for two months contributed to experimental variation in the ELISA reading (14% of the total variance) and should be avoided. Replicate assays of individual insects were shown to be unnecessary, since variation among replicate wells and plates was minor (2% of the total variance).

Triple collocation based merging of satellite soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

We propose a method for merging soil moisture retrievals from space borne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from u...

Preference uncertainty, preference learning, and paired comparison experiments

Treesearch

David C. Kingsley; Thomas C. Brown

2010-01-01

Results from paired comparison experiments suggest that as respondents progress through a sequence of binary choices they become more consistent, apparently fine-tuning their preferences. Consistency may be indicated by the variance of the estimated valuation distribution measured by the error term in the random utility model. A significant reduction in the variance is...

Two proposed convergence criteria for Monte Carlo solutions

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

Forster, R.A.; Pederson, S.P.; Booth, T.E.

1992-01-01

The central limit theorem (CLT) can be applied to a Monte Carlo solution if two requirements are satisfied: (1) The random variable has a finite mean and a finite variance; and (2) the number N of independent observations grows large. When these two conditions are satisfied, a confidence interval (CI) based on the normal distribution with a specified coverage probability can be formed. The first requirement is generally satisfied by the knowledge of the Monte Carlo tally being used. The Monte Carlo practitioner has a limited number of marginal methods to assess the fulfillment of the second requirement, such asmore » statistical error reduction proportional to 1/[radical]N with error magnitude guidelines. Two proposed methods are discussed in this paper to assist in deciding if N is large enough: estimating the relative variance of the variance (VOV) and examining the empirical history score probability density function (pdf).« less

Spacecraft methods and structures with enhanced attitude control that facilitates gyroscope substitutions

NASA Technical Reports Server (NTRS)

Li, Rongsheng (Inventor); Kurland, Jeffrey A. (Inventor); Dawson, Alec M. (Inventor); Wu, Yeong-Wei A. (Inventor); Uetrecht, David S. (Inventor)

2004-01-01

Methods and structures are provided that enhance attitude control during gyroscope substitutions by insuring that a spacecraft's attitude control system does not drive its absolute-attitude sensors out of their capture ranges. In a method embodiment, an operational process-noise covariance Q of a Kalman filter is temporarily replaced with a substantially greater interim process-noise covariance Q. This replacement increases the weight given to the most recent attitude measurements and hastens the reduction of attitude errors and gyroscope bias errors. The error effect of the substituted gyroscopes is reduced and the absolute-attitude sensors are not driven out of their capture range. In another method embodiment, this replacement is preceded by the temporary replacement of an operational measurement-noise variance R with a substantially larger interim measurement-noise variance R to reduce transients during the gyroscope substitutions.

Measurement properties and usability of non-contact scanners for measuring transtibial residual limb volume.

PubMed

Kofman, Rianne; Beekman, Anna M; Emmelot, Cornelis H; Geertzen, Jan H B; Dijkstra, Pieter U

2018-06-01

Non-contact scanners may have potential for measurement of residual limb volume. Different non-contact scanners have been introduced during the last decades. Reliability and usability (practicality and user friendliness) should be assessed before introducing these systems in clinical practice. The aim of this study was to analyze the measurement properties and usability of four non-contact scanners (TT Design, Omega Scanner, BioSculptor Bioscanner, and Rodin4D Scanner). Quasi experimental. Nine (geometric and residual limb) models were measured on two occasions, each consisting of two sessions, thus in total 4 sessions. In each session, four observers used the four systems for volume measurement. Mean for each model, repeatability coefficients for each system, variance components, and their two-way interactions of measurement conditions were calculated. User satisfaction was evaluated with the Post-Study System Usability Questionnaire. Systematic differences between the systems were found in volume measurements. Most of the variances were explained by the model (97%), while error variance was 3%. Measurement system and the interaction between system and model explained 44% of the error variance. Repeatability coefficient of the systems ranged from 0.101 (Omega Scanner) to 0.131 L (Rodin4D). Differences in Post-Study System Usability Questionnaire scores between the systems were small and not significant. The systems were reliable in determining residual limb volume. Measurement systems and the interaction between system and residual limb model explained most of the error variances. The differences in repeatability coefficient and usability between the four CAD/CAM systems were small. Clinical relevance If accurate measurements of residual limb volume are required (in case of research), modern non-contact scanners should be taken in consideration nowadays.

Improved uncertainty quantification in nondestructive assay for nonproliferation

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

Burr, Tom; Croft, Stephen; Jarman, Ken

2016-12-01

This paper illustrates methods to improve uncertainty quantification (UQ) for non-destructive assay (NDA) measurements used in nuclear nonproliferation. First, it is shown that current bottom-up UQ applied to calibration data is not always adequate, for three main reasons: (1) Because there are errors in both the predictors and the response, calibration involves a ratio of random quantities, and calibration data sets in NDA usually consist of only a modest number of samples (3–10); therefore, asymptotic approximations involving quantities needed for UQ such as means and variances are often not sufficiently accurate; (2) Common practice overlooks that calibration implies a partitioningmore » of total error into random and systematic error, and (3) In many NDA applications, test items exhibit non-negligible departures in physical properties from calibration items, so model-based adjustments are used, but item-specific bias remains in some data. Therefore, improved bottom-up UQ using calibration data should predict the typical magnitude of item-specific bias, and the suggestion is to do so by including sources of item-specific bias in synthetic calibration data that is generated using a combination of modeling and real calibration data. Second, for measurements of the same nuclear material item by both the facility operator and international inspectors, current empirical (top-down) UQ is described for estimating operator and inspector systematic and random error variance components. A Bayesian alternative is introduced that easily accommodates constraints on variance components, and is more robust than current top-down methods to the underlying measurement error distributions.« less

Noncommuting observables in quantum detection and estimation theory

NASA Technical Reports Server (NTRS)

Helstrom, C. W.

1972-01-01

Basing decisions and estimates on simultaneous approximate measurements of noncommuting observables in a quantum receiver is shown to be equivalent to measuring commuting projection operators on a larger Hilbert space than that of the receiver itself. The quantum-mechanical Cramer-Rao inequalities derived from right logarithmic derivatives and symmetrized logarithmic derivatives of the density operator are compared, and it is shown that the latter give superior lower bounds on the error variances of individual unbiased estimates of arrival time and carrier frequency of a coherent signal. For a suitably weighted sum of the error variances of simultaneous estimates of these, the former yield the superior lower bound under some conditions.

Ideal, nonideal, and no-marker variables: The confirmatory factor analysis (CFA) marker technique works when it matters.

PubMed

Williams, Larry J; O'Boyle, Ernest H

2015-09-01

A persistent concern in the management and applied psychology literature is the effect of common method variance on observed relations among variables. Recent work (i.e., Richardson, Simmering, & Sturman, 2009) evaluated 3 analytical approaches to controlling for common method variance, including the confirmatory factor analysis (CFA) marker technique. Their findings indicated significant problems with this technique, especially with nonideal marker variables (those with theoretical relations with substantive variables). Based on their simulation results, Richardson et al. concluded that not correcting for method variance provides more accurate estimates than using the CFA marker technique. We reexamined the effects of using marker variables in a simulation study and found the degree of error in estimates of a substantive factor correlation was relatively small in most cases, and much smaller than error associated with making no correction. Further, in instances in which the error was large, the correlations between the marker and substantive scales were higher than that found in organizational research with marker variables. We conclude that in most practical settings, the CFA marker technique yields parameter estimates close to their true values, and the criticisms made by Richardson et al. are overstated. (c) 2015 APA, all rights reserved).

Predictability Experiments With the Navy Operational Global Atmospheric Prediction System

NASA Astrophysics Data System (ADS)

Reynolds, C. A.; Gelaro, R.; Rosmond, T. E.

2003-12-01

There are several areas of research in numerical weather prediction and atmospheric predictability, such as targeted observations and ensemble perturbation generation, where it is desirable to combine information about the uncertainty of the initial state with information about potential rapid perturbation growth. Singular vectors (SVs) provide a framework to accomplish this task in a mathematically rigorous and computationally feasible manner. In this study, SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS). The analysis error variance information produced by the NRL Atmospheric Variational Data Assimilation System is used as the initial-time SV norm. These VAR SVs are compared to SVs for which total energy is both the initial and final time norms (TE SVs). The incorporation of analysis error variance information has a significant impact on the structure and location of the SVs. This in turn has a significant impact on targeted observing applications. The utility and implications of such experiments in assessing the analysis error variance estimates will be explored. Computing support has been provided by the Department of Defense High Performance Computing Center at the Naval Oceanographic Office Major Shared Resource Center at Stennis, Mississippi.

Cost effective stream-gaging strategies for the Lower Colorado River basin; the Blythe field office operations

USGS Publications Warehouse

Moss, Marshall E.; Gilroy, Edward J.

1980-01-01

This report describes the theoretical developments and illustrates the applications of techniques that recently have been assembled to analyze the cost-effectiveness of federally funded stream-gaging activities in support of the Colorado River compact and subsequent adjudications. The cost effectiveness of 19 stream gages in terms of minimizing the sum of the variances of the errors of estimation of annual mean discharge is explored by means of a sequential-search optimization scheme. The search is conducted over a set of decision variables that describes the number of times that each gaging route is traveled in a year. A gage route is defined as the most expeditious circuit that is made from a field office to visit one or more stream gages and return to the office. The error variance is defined as a function of the frequency of visits to a gage by using optimal estimation theory. Currently a minimum of 12 visits per year is made to any gage. By changing to a six-visit minimum, the same total error variance can be attained for the 19 stations with a budget of 10% less than the current one. Other strategies are also explored. (USGS)

Interruption Practice Reduces Errors

DTIC Science & Technology

2014-01-01

dangers of errors at the PCS. Electronic health record systems are used to reduce certain errors related to poor- handwriting and dosage...10.16, MSE =.31, p< .05, η2 = .18 A significant interaction between the number of interruptions and interrupted trials suggests that trials...the variance when calculating whether a memory has a higher signal than interference. If something in addition to activation contributes to goal

Adaptive Resampling Particle Filters for GPS Carrier-Phase Navigation and Collision Avoidance System

NASA Astrophysics Data System (ADS)

Hwang, Soon Sik

This dissertation addresses three problems: 1) adaptive resampling technique (ART) for Particle Filters, 2) precise relative positioning using Global Positioning System (GPS) Carrier-Phase (CP) measurements applied to nonlinear integer resolution problem for GPS CP navigation using Particle Filters, and 3) collision detection system based on GPS CP broadcasts. First, Monte Carlo filters, called Particle Filters (PF), are widely used where the system is non-linear and non-Gaussian. In real-time applications, their estimation accuracies and efficiencies are significantly affected by the number of particles and the scheduling of relocating weights and samples, the so-called resampling step. In this dissertation, the appropriate number of particles is estimated adaptively such that the error of the sample mean and variance stay in bounds. These bounds are given by the confidence interval of a normal probability distribution for a multi-variate state. Two required number of samples maintaining the mean and variance error within the bounds are derived. The time of resampling is determined when the required sample number for the variance error crosses the required sample number for the mean error. Second, the PF using GPS CP measurements with adaptive resampling is applied to precise relative navigation between two GPS antennas. In order to make use of CP measurements for navigation, the unknown number of cycles between GPS antennas, the so called integer ambiguity, should be resolved. The PF is applied to this integer ambiguity resolution problem where the relative navigation states estimation involves nonlinear observations and nonlinear dynamics equation. Using the PF, the probability density function of the states is estimated by sampling from the position and velocity space and the integer ambiguities are resolved without using the usual hypothesis tests to search for the integer ambiguity. The ART manages the number of position samples and the frequency of the resampling step for real-time kinematics GPS navigation. The experimental results demonstrate the performance of the ART and the insensitivity of the proposed approach to GPS CP cycle-slips. Third, the GPS has great potential for the development of new collision avoidance systems and is being considered for the next generation Traffic alert and Collision Avoidance System (TCAS). The current TCAS equipment, is capable of broadcasting GPS code information to nearby airplanes, and also, the collision avoidance system using the navigation information based on GPS code has been studied by researchers. In this dissertation, the aircraft collision detection system using GPS CP information is addressed. The PF with position samples is employed for the CP based relative position estimation problem and the same algorithm can be used to determine the vehicle attitude if multiple GPS antennas are used. For a reliable and enhanced collision avoidance system, three dimensional trajectories are projected using the estimates of the relative position, velocity, and the attitude. It is shown that the performance of GPS CP based collision detecting algorithm meets the accuracy requirements for a precise approach of flight for auto landing with significantly less unnecessary collision false alarms and no miss alarms.

Bootstrap Estimates of Standard Errors in Generalizability Theory

ERIC Educational Resources Information Center

Tong, Ye; Brennan, Robert L.

2007-01-01

Estimating standard errors of estimated variance components has long been a challenging task in generalizability theory. Researchers have speculated about the potential applicability of the bootstrap for obtaining such estimates, but they have identified problems (especially bias) in using the bootstrap. Using Brennan's bias-correcting procedures…

Analysis of Wind Tunnel Polar Replicates Using the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

Deloach, Richard; Micol, John R.

2010-01-01

The role of variance in a Modern Design of Experiments analysis of wind tunnel data is reviewed, with distinctions made between explained and unexplained variance. The partitioning of unexplained variance into systematic and random components is illustrated, with examples of the elusive systematic component provided for various types of real-world tests. The importance of detecting and defending against systematic unexplained variance in wind tunnel testing is discussed, and the random and systematic components of unexplained variance are examined for a representative wind tunnel data set acquired in a test in which a missile is used as a test article. The adverse impact of correlated (non-independent) experimental errors is described, and recommendations are offered for replication strategies that facilitate the quantification of random and systematic unexplained variance.

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.

Additive Partial Least Squares for efficient modelling of independent variance sources demonstrated on practical case studies.

PubMed

Luoma, Pekka; Natschläger, Thomas; Malli, Birgit; Pawliczek, Marcin; Brandstetter, Markus

2018-05-12

A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of phase error effects in multishot diffusion-prepared turbo spin echo imaging

PubMed Central

Cervantes, Barbara; Kooijman, Hendrik; Karampinos, Dimitrios C.

2017-01-01

Background To characterize the effect of phase errors on the magnitude and the phase of the diffusion-weighted (DW) signal acquired with diffusion-prepared turbo spin echo (dprep-TSE) sequences. Methods Motion and eddy currents were identified as the main sources of phase errors. An analytical expression for the effect of phase errors on the acquired signal was derived and verified using Bloch simulations, phantom, and in vivo experiments. Results Simulations and experiments showed that phase errors during the diffusion preparation cause both magnitude and phase modulation on the acquired data. When motion-induced phase error (MiPe) is accounted for (e.g., with motion-compensated diffusion encoding), the signal magnitude modulation due to the leftover eddy-current-induced phase error cannot be eliminated by the conventional phase cycling and sum-of-squares (SOS) method. By employing magnitude stabilizers, the phase-error-induced magnitude modulation, regardless of its cause, was removed but the phase modulation remained. The in vivo comparison between pulsed gradient and flow-compensated diffusion preparations showed that MiPe needed to be addressed in multi-shot dprep-TSE acquisitions employing magnitude stabilizers. Conclusions A comprehensive analysis of phase errors in dprep-TSE sequences showed that magnitude stabilizers are mandatory in removing the phase error induced magnitude modulation. Additionally, when multi-shot dprep-TSE is employed the inconsistent signal phase modulation across shots has to be resolved before shot-combination is performed. PMID:28516049

Identification of patient information corruption in the intensive care unit: using a scoring tool to direct quality improvements in handover.

PubMed

Pickering, Brian W; Hurley, Killian; Marsh, Brian

2009-11-01

To use a handover assessment tool for identifying patient information corruption and objectively evaluating interventions designed to reduce handover errors and improve medical decision making. The continuous monitoring, intervention, and evaluation of the patient in modern intensive care unit practice generates large quantities of information, the platform on which medical decisions are made. Information corruption, defined as errors of distortion/omission compared with the medical record, may result in medical judgment errors. Identifying these errors may lead to quality improvements in intensive care unit care delivery and safety. Handover assessment instrument development study divided into two phases by the introduction of a handover intervention. Closed, 17-bed, university-affiliated mixed surgical/medical intensive care unit. Senior and junior medical members of the intensive care unit team. Electronic handover page. Study subjects were asked to recall clinical information commonly discussed at handover on individual patients. The handover score measured the percentage of information correctly retained for each individual doctor-patient interaction. The clinical intention score, a subjective measure of medical judgment, was graded (1-5) by three blinded intensive care unit experts. A total of 137 interactions were scored. Median (interquartile range) handover scores for phases 1 and 2 were 79.07% (67.44-84.50) and 83.72% (76.16-88.37), respectively. Score variance was reduced by the handover intervention (p < .05). Increasing median handover scores, 68.60 to 83.72, were associated with increases in clinical intention scores from 1 to 5 (chi-square = 23.59, df = 4, p < .0001). When asked to recall clinical information discussed at handover, medical members of the intensive care unit team provide data that are significantly corrupted compared with the medical record. Low subjective clinical judgment scores are significant associated with low handover scores. The handover/clinical intention scores may, therefore, be useful screening tools for intensive care unit system vulnerability to medical error. Additionally, handover instruments can identify interventions that reduce system vulnerability to error and may be used to guide quality improvements in handover practice.

A Minimum Variance Algorithm for Overdetermined TOA Equations with an Altitude Constraint.

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

Romero, Louis A; Mason, John J.

We present a direct (non-iterative) method for solving for the location of a radio frequency (RF) emitter, or an RF navigation receiver, using four or more time of arrival (TOA) measurements and an assumed altitude above an ellipsoidal earth. Both the emitter tracking problem and the navigation application are governed by the same equations, but with slightly different interpreta- tions of several variables. We treat the assumed altitude as a soft constraint, with a specified noise level, just as the TOA measurements are handled, with their respective noise levels. With 4 or more TOA measurements and the assumed altitude, themore » problem is overdetermined and is solved in the weighted least squares sense for the 4 unknowns, the 3-dimensional position and time. We call the new technique the TAQMV (TOA Altitude Quartic Minimum Variance) algorithm, and it achieves the minimum possible error variance for given levels of TOA and altitude estimate noise. The method algebraically produces four solutions, the least-squares solution, and potentially three other low residual solutions, if they exist. In the lightly overdermined cases where multiple local minima in the residual error surface are more likely to occur, this algebraic approach can produce all of the minima even when an iterative approach fails to converge. Algorithm performance in terms of solution error variance and divergence rate for bas eline (iterative) and proposed approach are given in tables.« less

Statistical methodology for estimating the mean difference in a meta-analysis without study-specific variance information.

PubMed

Sangnawakij, Patarawan; Böhning, Dankmar; Adams, Stephen; Stanton, Michael; Holling, Heinz

2017-04-30

Statistical inference for analyzing the results from several independent studies on the same quantity of interest has been investigated frequently in recent decades. Typically, any meta-analytic inference requires that the quantity of interest is available from each study together with an estimate of its variability. The current work is motivated by a meta-analysis on comparing two treatments (thoracoscopic and open) of congenital lung malformations in young children. Quantities of interest include continuous end-points such as length of operation or number of chest tube days. As studies only report mean values (and no standard errors or confidence intervals), the question arises how meta-analytic inference can be developed. We suggest two methods to estimate study-specific variances in such a meta-analysis, where only sample means and sample sizes are available in the treatment arms. A general likelihood ratio test is derived for testing equality of variances in two groups. By means of simulation studies, the bias and estimated standard error of the overall mean difference from both methodologies are evaluated and compared with two existing approaches: complete study analysis only and partial variance information. The performance of the test is evaluated in terms of type I error. Additionally, we illustrate these methods in the meta-analysis on comparing thoracoscopic and open surgery for congenital lung malformations and in a meta-analysis on the change in renal function after kidney donation. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models.

PubMed

Garcia, Tanya P; Ma, Yanyuan

2017-10-01

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance-covariance. We construct root- n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both. Empirical results demonstrate robustness to different incorrect working models in homoscedastic and heteroskedastic models with error-prone covariates.

Effects of Differential Item Functioning on Examinees' Test Performance and Reliability of Test

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; Zhang, Jinming

2017-01-01

Simulations were conducted to examine the effect of differential item functioning (DIF) on measurement consequences such as total scores, item response theory (IRT) ability estimates, and test reliability in terms of the ratio of true-score variance to observed-score variance and the standard error of estimation for the IRT ability parameter. The…

An Investigation of the Raudenbush (1988) Test for Studying Variance Heterogeneity.

ERIC Educational Resources Information Center

Harwell, Michael

1997-01-01

The meta-analytic method proposed by S. W. Raudenbush (1988) for studying variance heterogeneity was studied. Results of a Monte Carlo study indicate that the Type I error rate of the test is sensitive to even modestly platykurtic score distributions and to the ratio of study sample size to the number of studies. (SLD)

Stabilizing Conditional Standard Errors of Measurement in Scale Score Transformations

ERIC Educational Resources Information Center

Moses, Tim; Kim, YoungKoung

2017-01-01

The focus of this article is on scale score transformations that can be used to stabilize conditional standard errors of measurement (CSEMs). Three transformations for stabilizing the estimated CSEMs are reviewed, including the traditional arcsine transformation, a recently developed general variance stabilization transformation, and a new method…

New Statistical Techniques for Evaluating Longitudinal Models.

ERIC Educational Resources Information Center

Murray, James R.; Wiley, David E.

A basic methodological approach in developmental studies is the collection of longitudinal data. Behavioral data cen take at least two forms, qualitative (or discrete) and quantitative. Both types are fallible. Measurement errors can occur in quantitative data and measures of these are based on error variance. Qualitative or discrete data can…

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.…

The error structure of the SMAP single and dual channel soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of erro...

Analysis of stimulus-related activity in rat auditory cortex using complex spectral coefficients

PubMed Central

Krause, Bryan M.

2013-01-01

The neural mechanisms of sensory responses recorded from the scalp or cortical surface remain controversial. Evoked vs. induced response components (i.e., changes in mean vs. variance) are associated with bottom-up vs. top-down processing, but trial-by-trial response variability can confound this interpretation. Phase reset of ongoing oscillations has also been postulated to contribute to sensory responses. In this article, we present evidence that responses under passive listening conditions are dominated by variable evoked response components. We measured the mean, variance, and phase of complex time-frequency coefficients of epidurally recorded responses to acoustic stimuli in rats. During the stimulus, changes in mean, variance, and phase tended to co-occur. After the stimulus, there was a small, low-frequency offset response in the mean and modest, prolonged desynchronization in the alpha band. Simulations showed that trial-by-trial variability in the mean can account for most of the variance and phase changes observed during the stimulus. This variability was state dependent, with smallest variability during periods of greatest arousal. Our data suggest that cortical responses to auditory stimuli reflect variable inputs to the cortical network. These analyses suggest that caution should be exercised when interpreting variance and phase changes in terms of top-down cortical processing. PMID:23657279

Calibration and error analysis of metal-oxide-semiconductor field-effect transistor dosimeters for computed tomography radiation dosimetry.

PubMed

Trattner, Sigal; Prinsen, Peter; Wiegert, Jens; Gerland, Elazar-Lars; Shefer, Efrat; Morton, Tom; Thompson, Carla M; Yagil, Yoad; Cheng, Bin; Jambawalikar, Sachin; Al-Senan, Rani; Amurao, Maxwell; Halliburton, Sandra S; Einstein, Andrew J

2017-12-01

Metal-oxide-semiconductor field-effect transistors (MOSFETs) serve as a helpful tool for organ radiation dosimetry and their use has grown in computed tomography (CT). While different approaches have been used for MOSFET calibration, those using the commonly available 100 mm pencil ionization chamber have not incorporated measurements performed throughout its length, and moreover, no previous work has rigorously evaluated the multiple sources of error involved in MOSFET calibration. In this paper, we propose a new MOSFET calibration approach to translate MOSFET voltage measurements into absorbed dose from CT, based on serial measurements performed throughout the length of a 100-mm ionization chamber, and perform an analysis of the errors of MOSFET voltage measurements and four sources of error in calibration. MOSFET calibration was performed at two sites, to determine single calibration factors for tube potentials of 80, 100, and 120 kVp, using a 100-mm-long pencil ion chamber and a cylindrical computed tomography dose index (CTDI) phantom of 32 cm diameter. The dose profile along the 100-mm ion chamber axis was sampled in 5 mm intervals by nine MOSFETs in the nine holes of the CTDI phantom. Variance of the absorbed dose was modeled as a sum of the MOSFET voltage measurement variance and the calibration factor variance, the latter being comprised of three main subcomponents: ionization chamber reading variance, MOSFET-to-MOSFET variation and a contribution related to the fact that the average calibration factor of a few MOSFETs was used as an estimate for the average value of all MOSFETs. MOSFET voltage measurement error was estimated based on sets of repeated measurements. The calibration factor overall voltage measurement error was calculated from the above analysis. Calibration factors determined were close to those reported in the literature and by the manufacturer (~3 mV/mGy), ranging from 2.87 to 3.13 mV/mGy. The error σ V of a MOSFET voltage measurement was shown to be proportional to the square root of the voltage V: σV=cV where c = 0.11 mV. A main contributor to the error in the calibration factor was the ionization chamber reading error with 5% error. The usage of a single calibration factor for all MOSFETs introduced an additional error of about 5-7%, depending on the number of MOSFETs that were used to determine the single calibration factor. The expected overall error in a high-dose region (~30 mGy) was estimated to be about 8%, compared to 6% when an individual MOSFET calibration was performed. For a low-dose region (~3 mGy), these values were 13% and 12%. A MOSFET calibration method was developed using a 100-mm pencil ion chamber and a CTDI phantom, accompanied by an absorbed dose error analysis reflecting multiple sources of measurement error. When using a single calibration factor, per tube potential, for different MOSFETs, only a small error was introduced into absorbed dose determinations, thus supporting the use of a single calibration factor for experiments involving many MOSFETs, such as those required to accurately estimate radiation effective dose. © 2017 American Association of Physicists in Medicine.

Smoothing of the bivariate LOD score for non-normal quantitative traits.

PubMed

Buil, Alfonso; Dyer, Thomas D; Almasy, Laura; Blangero, John

2005-12-30

Variance component analysis provides an efficient method for performing linkage analysis for quantitative traits. However, type I error of variance components-based likelihood ratio testing may be affected when phenotypic data are non-normally distributed (especially with high values of kurtosis). This results in inflated LOD scores when the normality assumption does not hold. Even though different solutions have been proposed to deal with this problem with univariate phenotypes, little work has been done in the multivariate case. We present an empirical approach to adjust the inflated LOD scores obtained from a bivariate phenotype that violates the assumption of normality. Using the Collaborative Study on the Genetics of Alcoholism data available for the Genetic Analysis Workshop 14, we show how bivariate linkage analysis with leptokurtotic traits gives an inflated type I error. We perform a novel correction that achieves acceptable levels of type I error.

Measurement error associated with surveys of fish abundance in Lake Michigan

USGS Publications Warehouse

Krause, Ann E.; Hayes, Daniel B.; Bence, James R.; Madenjian, Charles P.; Stedman, Ralph M.

2002-01-01

In fisheries, imprecise measurements in catch data from surveys adds uncertainty to the results of fishery stock assessments. The USGS Great Lakes Science Center (GLSC) began to survey the fall fish community of Lake Michigan in 1962 with bottom trawls. The measurement error was evaluated at the level of individual tows for nine fish species collected in this survey by applying a measurement-error regression model to replicated trawl data. It was found that the estimates of measurement-error variance ranged from 0.37 (deepwater sculpin, Myoxocephalus thompsoni) to 1.23 (alewife, Alosa pseudoharengus) on a logarithmic scale corresponding to a coefficient of variation = 66% to 156%. The estimates appeared to increase with the range of temperature occupied by the fish species. This association may be a result of the variability in the fall thermal structure of the lake. The estimates may also be influenced by other factors, such as pelagic behavior and schooling. Measurement error might be reduced by surveying the fish community during other seasons and/or by using additional technologies, such as acoustics. Measurement-error estimates should be considered when interpreting results of assessments that use abundance information from USGS-GLSC surveys of Lake Michigan and could be used if the survey design was altered. This study is the first to report estimates of measurement-error variance associated with this survey.

Variability of the Degassing Flux of 4He as an impact of 4He -Dating of Groundwaters

NASA Astrophysics Data System (ADS)

Torgersen, T.

2009-12-01

4He dating of groundwater is often confounded by an external flux of 4He as the result of a crustal degassing. Estimates of this external flux have been made but what is the impact on estimates of the 4He groundwater age? The existing measures of the 4He flux across the Earth’s solid surface have been evaluated collectively. The time-and-area weighted arithmetic mean (standard deviation) of n=33 4He degassing fluxes is 3.32(±0.45) x 1010 4He atoms m-2s-1. The log normal mean of 271 measures of the flux into Precambrian shield lakes of Canada is 4.57 x 1010atoms 4He m-2s-1 with a variance of */3.9x. The log normal mean of measurements (n=33) of the crustal flux is 3.63 x 1010 4He m-2s-1 with a best estimate one sigma log normal error of */36x based on an assumption of symmetric error bars. (For comparison, the log normal mean heat flow is 62.2 mW m-2 with a log normal variance of */1.8x; the best estimate mean is 65±1.6 Wm-2, Polach et al., 1993). The variance of the continental flux is shown to increase with decreasing time scales (*/ ~106x at 0.5yr) and decreasing space scales (*/ ~106x at 1km) suggesting that the mechanisms of crustal helium transport and degassing contain a high degree of spatial and temporal variability. This best estimate of the mean and variance in the flux of 4He from continents remains approximately equivalent to the radiogenic production rate of 4He in the whole crust. The small degree of variance in the Canadian lake data (n=271), Precambrian terrain, suggests that it may represent a best approximation of “steady state” crustal degassing. Large scale vertical mass transport in continental crust is estimated as scaled values to be of the order 10-5 cm2s-1 for helium (over 2Byr and 40km vertically) vs. 10-2 cm2s-1 for heat. The mass transport rate requires not only release of 4He from the solid phase via fracturing or comminution but also an enhanced rate of mass transport facilitated by some degree of fluid advection (as has been suggested by metamorphic geology) and further imply a separation of heat and mass during transport.

An internal pilot design for prospective cancer screening trials with unknown disease prevalence.

PubMed

Brinton, John T; Ringham, Brandy M; Glueck, Deborah H

2015-10-13

For studies that compare the diagnostic accuracy of two screening tests, the sample size depends on the prevalence of disease in the study population, and on the variance of the outcome. Both parameters may be unknown during the design stage, which makes finding an accurate sample size difficult. To solve this problem, we propose adapting an internal pilot design. In this adapted design, researchers will accrue some percentage of the planned sample size, then estimate both the disease prevalence and the variances of the screening tests. The updated estimates of the disease prevalence and variance are used to conduct a more accurate power and sample size calculation. We demonstrate that in large samples, the adapted internal pilot design produces no Type I inflation. For small samples (N less than 50), we introduce a novel adjustment of the critical value to control the Type I error rate. We apply the method to two proposed prospective cancer screening studies: 1) a small oral cancer screening study in individuals with Fanconi anemia and 2) a large oral cancer screening trial. Conducting an internal pilot study without adjusting the critical value can cause Type I error rate inflation in small samples, but not in large samples. An internal pilot approach usually achieves goal power and, for most studies with sample size greater than 50, requires no Type I error correction. Further, we have provided a flexible and accurate approach to bound Type I error below a goal level for studies with small sample size.

The Quantum Phase-Dynamical Properties of the Squeezed Vacuum State Intensity-Couple Interacting with the Atom

NASA Technical Reports Server (NTRS)

Fan, An-Fu; Sun, Nian-Chun; Zhou, Xin

1996-01-01

The Phase-dynamical properties of the squeezed vacuum state intensity-couple interacting with the two-level atom in an ideal cavity are studied using the Hermitian phase operator formalism. Exact general expressions for the phase distribution and the associated expectation value and variance of the phase operator have been derived. we have also obtained the analytic results of the phase variance for two special cases-weakly and strongly squeezed vacuum. The results calculated numerically show that squeezing has a significant effect on the phase properties of squeezed vacuum.

Statistical methods for biodosimetry in the presence of both Berkson and classical measurement error

NASA Astrophysics Data System (ADS)

Miller, Austin

In radiation epidemiology, the true dose received by those exposed cannot be assessed directly. Physical dosimetry uses a deterministic function of the source term, distance and shielding to estimate dose. For the atomic bomb survivors, the physical dosimetry system is well established. The classical measurement errors plaguing the location and shielding inputs to the physical dosimetry system are well known. Adjusting for the associated biases requires an estimate for the classical measurement error variance, for which no data-driven estimate exists. In this case, an instrumental variable solution is the most viable option to overcome the classical measurement error indeterminacy. Biological indicators of dose may serve as instrumental variables. Specification of the biodosimeter dose-response model requires identification of the radiosensitivity variables, for which we develop statistical definitions and variables. More recently, researchers have recognized Berkson error in the dose estimates, introduced by averaging assumptions for many components in the physical dosimetry system. We show that Berkson error induces a bias in the instrumental variable estimate of the dose-response coefficient, and then address the estimation problem. This model is specified by developing an instrumental variable mixed measurement error likelihood function, which is then maximized using a Monte Carlo EM Algorithm. These methods produce dose estimates that incorporate information from both physical and biological indicators of dose, as well as the first instrumental variable based data-driven estimate for the classical measurement error variance.

Calibration Method to Eliminate Zeroth Order Effect in Lateral Shearing Interferometry

NASA Astrophysics Data System (ADS)

Fang, Chao; Xiang, Yang; Qi, Keqi; Chen, Dawei

2018-04-01

In this paper, a calibration method is proposed which eliminates the zeroth order effect in lateral shearing interferometry. An analytical expression of the calibration error function is deduced, and the relationship between the phase-restoration error and calibration error is established. The analytical results show that the phase-restoration error introduced by the calibration error is proportional to the phase shifting error and zeroth order effect. The calibration method is verified using simulations and experiments. The simulation results show that the phase-restoration error is approximately proportional to the phase shift error and zeroth order effect, when the phase shifting error is less than 2° and the zeroth order effect is less than 0.2. The experimental result shows that compared with the conventional method with 9-frame interferograms, the calibration method with 5-frame interferograms achieves nearly the same restoration accuracy.

Modeling conflict and error in the medial frontal cortex.

PubMed

Mayer, Andrew R; Teshiba, Terri M; Franco, Alexandre R; Ling, Josef; Shane, Matthew S; Stephen, Julia M; Jung, Rex E

2012-12-01

Despite intensive study, the role of the dorsal medial frontal cortex (dMFC) in error monitoring and conflict processing remains actively debated. The current experiment manipulated conflict type (stimulus conflict only or stimulus and response selection conflict) and utilized a novel modeling approach to isolate error and conflict variance during a multimodal numeric Stroop task. Specifically, hemodynamic response functions resulting from two statistical models that either included or isolated variance arising from relatively few error trials were directly contrasted. Twenty-four participants completed the task while undergoing event-related functional magnetic resonance imaging on a 1.5-Tesla scanner. Response times monotonically increased based on the presence of pure stimulus or stimulus and response selection conflict. Functional results indicated that dMFC activity was present during trials requiring response selection and inhibition of competing motor responses, but absent during trials involving pure stimulus conflict. A comparison of the different statistical models suggested that relatively few error trials contributed to a disproportionate amount of variance (i.e., activity) throughout the dMFC, but particularly within the rostral anterior cingulate gyrus (rACC). Finally, functional connectivity analyses indicated that an empirically derived seed in the dorsal ACC/pre-SMA exhibited strong connectivity (i.e., positive correlation) with prefrontal and inferior parietal cortex but was anti-correlated with the default-mode network. An empirically derived seed from the rACC exhibited the opposite pattern, suggesting that sub-regions of the dMFC exhibit different connectivity patterns with other large scale networks implicated in internal mentations such as daydreaming (default-mode) versus the execution of top-down attentional control (fronto-parietal). Copyright © 2011 Wiley Periodicals, Inc.

A Negative Binomial Regression Model for Accuracy Tests

ERIC Educational Resources Information Center

Hung, Lai-Fa

2012-01-01

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an…

Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error

NASA Astrophysics Data System (ADS)

El-Diasty, M.; El-Rabbany, A.; Pagiatakis, S.

2007-11-01

We examine the effect of varying the temperature points on MEMS inertial sensors' noise models using Allan variance and least-squares spectral analysis (LSSA). Allan variance is a method of representing root-mean-square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected at different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of the two MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors' stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors' stochastic model parameters are temperature dependent. Also, the Kaiser window FIR low-pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.

Estimating the Autocorrelated Error Model with Trended Data: Further Results,

DTIC Science & Technology

1979-11-01

Perhaps the most serious deficiency of OLS in the presence of autocorrelation is not inefficiency but bias in its estimated standard errors--a bias...k for all t has variance var(b) = o2/ Tk2 2This refutes Maeshiro’s (1976) conjecture that "an estimator utilizing relevant extraneous information

Comment on 3PL IRT Adjustment for Guessing

ERIC Educational Resources Information Center

Chiu, Ting-Wei; Camilli, Gregory

2013-01-01

Guessing behavior is an issue discussed widely with regard to multiple choice tests. Its primary effect is on number-correct scores for examinees at lower levels of proficiency. This is a systematic error or bias, which increases observed test scores. Guessing also can inflate random error variance. Correction or adjustment for guessing formulas…

Circular carrier squeezing interferometry: Suppressing phase shift error in simultaneous phase-shifting point-diffraction interferometer

NASA Astrophysics Data System (ADS)

Zheng, Donghui; Chen, Lei; Li, Jinpeng; Sun, Qinyuan; Zhu, Wenhua; Anderson, James; Zhao, Jian; Schülzgen, Axel

2018-03-01

Circular carrier squeezing interferometry (CCSI) is proposed and applied to suppress phase shift error in simultaneous phase-shifting point-diffraction interferometer (SPSPDI). By introducing a defocus, four phase-shifting point-diffraction interferograms with circular carrier are acquired, and then converted into linear carrier interferograms by a coordinate transform. Rearranging the transformed interferograms into a spatial-temporal fringe (STF), so the error lobe will be separated from the phase lobe in the Fourier spectrum of the STF, and filtering the phase lobe to calculate the extended phase, when combined with the corresponding inverse coordinate transform, exactly retrieves the initial phase. Both simulations and experiments validate the ability of CCSI to suppress the ripple error generated by the phase shift error. Compared with carrier squeezing interferometry (CSI), CCSI is effective on some occasions in which a linear carrier is difficult to introduce, and with the added benefit of eliminating retrace error.

Chaotropic salts in liquid chromatographic method development for the determination of pramipexole and its impurities following quality-by-design principles.

PubMed

Vemić, Ana; Rakić, Tijana; Malenović, Anđelija; Medenica, Mirjana

2015-01-01

The aim of this paper is to present a development of liquid chromatographic method when chaotropic salts are used as mobile phase additives following the QbD principles. The effect of critical process parameters (column chemistry, salt nature and concentration, acetonitrile content and column temperature) on the critical quality attributes (retention of the first and last eluting peak and separation of the critical peak pairs) was studied applying the design of experiments-design space methodology (DoE-DS). D-optimal design is chosen in order to simultaneously examine both categorical and numerical factors in minimal number of experiments. Two ways for the achievement of quality assurance were performed and compared. Namely, the uncertainty originating from the models was assessed by Monte Carlo simulations propagating the error equal to the variance of the model residuals and propagating the error originating from the model coefficients' calculation. The baseline separation of pramipexole and its five impurities is achieved fulfilling all the required criteria while the method validation proved its reliability. Copyright © 2014 Elsevier B.V. All rights reserved.

Counting OCR errors in typeset text

NASA Astrophysics Data System (ADS)

Sandberg, Jonathan S.

1995-03-01

Frequently object recognition accuracy is a key component in the performance analysis of pattern matching systems. In the past three years, the results of numerous excellent and rigorous studies of OCR system typeset-character accuracy (henceforth OCR accuracy) have been published, encouraging performance comparisons between a variety of OCR products and technologies. These published figures are important; OCR vendor advertisements in the popular trade magazines lead readers to believe that published OCR accuracy figures effect market share in the lucrative OCR market. Curiously, a detailed review of many of these OCR error occurrence counting results reveals that they are not reproducible as published and they are not strictly comparable due to larger variances in the counts than would be expected by the sampling variance. Naturally, since OCR accuracy is based on a ratio of the number of OCR errors over the size of the text searched for errors, imprecise OCR error accounting leads to similar imprecision in OCR accuracy. Some published papers use informal, non-automatic, or intuitively correct OCR error accounting. Still other published results present OCR error accounting methods based on string matching algorithms such as dynamic programming using Levenshtein (edit) distance but omit critical implementation details (such as the existence of suspect markers in the OCR generated output or the weights used in the dynamic programming minimization procedure). The problem with not specifically revealing the accounting method is that the number of errors found by different methods are significantly different. This paper identifies the basic accounting methods used to measure OCR errors in typeset text and offers an evaluation and comparison of the various accounting methods.

Motor impulsivity during childhood and adolescence: a longitudinal biometric analysis of the go/no-go task in 9- to 18-year-old twins.

PubMed

Bezdjian, Serena; Tuvblad, Catherine; Wang, Pan; Raine, Adrian; Baker, Laura A

2014-11-01

In the present study, we investigated genetic and environmental effects on motor impulsivity from childhood to late adolescence using a longitudinal sample of twins from ages 9 to 18 years. Motor impulsivity was assessed using errors of commission (no-go errors) in a visual go/no-go task at 4 time points: ages 9-10, 11-13, 14-15, and 16-18 years. Significant genetic and nonshared environmental effects on motor impulsivity were found at each of the 4 waves of assessment with genetic factors explaining 22%-41% of the variance within each of the 4 waves. Phenotypically, children's average performance improved across age (i.e., fewer no-go errors during later assessments). Multivariate biometric analyses revealed that common genetic factors influenced 12%-40% of the variance in motor impulsivity across development, whereas nonshared environmental factors common to all time points contributed to 2%-52% of the variance. Nonshared environmental influences specific to each time point also significantly influenced motor impulsivity. Overall, results demonstrated that although genetic factors were critical to motor impulsivity across development, both common and specific nonshared environmental factors played a strong role in the development of motor impulsivity across age. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

NASA Astrophysics Data System (ADS)

Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

2017-09-01

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

Variance of discharge estimates sampled using acoustic Doppler current profilers from moving boats

USGS Publications Warehouse

Garcia, Carlos M.; Tarrab, Leticia; Oberg, Kevin; Szupiany, Ricardo; Cantero, Mariano I.

2012-01-01

This paper presents a model for quantifying the random errors (i.e., variance) of acoustic Doppler current profiler (ADCP) discharge measurements from moving boats for different sampling times. The model focuses on the random processes in the sampled flow field and has been developed using statistical methods currently available for uncertainty analysis of velocity time series. Analysis of field data collected using ADCP from moving boats from three natural rivers of varying sizes and flow conditions shows that, even though the estimate of the integral time scale of the actual turbulent flow field is larger than the sampling interval, the integral time scale of the sampled flow field is on the order of the sampling interval. Thus, an equation for computing the variance error in discharge measurements associated with different sampling times, assuming uncorrelated flow fields is appropriate. The approach is used to help define optimal sampling strategies by choosing the exposure time required for ADCPs to accurately measure flow discharge.

Monte Carlo Simulations Comparing Fisher Exact Test and Unequal Variances t Test for Analysis of Differences Between Groups in Brief Hospital Lengths of Stay.

PubMed

Dexter, Franklin; Bayman, Emine O; Dexter, Elisabeth U

2017-12-01

We examined type I and II error rates for analysis of (1) mean hospital length of stay (LOS) versus (2) percentage of hospital LOS that are overnight. These 2 end points are suitable for when LOS is treated as a secondary economic end point. We repeatedly resampled LOS for 5052 discharges of thoracoscopic wedge resections and lung lobectomy at 26 hospitals. Unequal variances t test (Welch method) and Fisher exact test both were conservative (ie, type I error rate less than nominal level). The Wilcoxon rank sum test was included as a comparator; the type I error rates did not differ from the nominal level of 0.05 or 0.01. Fisher exact test was more powerful than the unequal variances t test at detecting differences among hospitals; estimated odds ratio for obtaining P < .05 with Fisher exact test versus unequal variances t test = 1.94, with 95% confidence interval, 1.31-3.01. Fisher exact test and Wilcoxon-Mann-Whitney had comparable statistical power in terms of differentiating LOS between hospitals. For studies with LOS to be used as a secondary end point of economic interest, there is currently considerable interest in the planned analysis being for the percentage of patients suitable for ambulatory surgery (ie, hospital LOS equals 0 or 1 midnight). Our results show that there need not be a loss of statistical power when groups are compared using this binary end point, as compared with either Welch method or Wilcoxon rank sum test.

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications.

PubMed

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

PubMed Central

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Background Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. Methodology/Principal findings The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. Conclusion/Significance The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates. PMID:24489839

Canceling the momentum in a phase-shifting algorithm to eliminate spatially uniform errors.

PubMed

Hibino, Kenichi; Kim, Yangjin

2016-08-10

In phase-shifting interferometry, phase modulation nonlinearity causes both spatially uniform and nonuniform errors in the measured phase. Conventional linear-detuning error-compensating algorithms only eliminate the spatially variable error component. The uniform error is proportional to the inertial momentum of the data-sampling weight of a phase-shifting algorithm. This paper proposes a design approach to cancel the momentum by using characteristic polynomials in the Z-transform space and shows that an arbitrary M-frame algorithm can be modified to a new (M+2)-frame algorithm that acquires new symmetry to eliminate the uniform error.

A Study on Mutil-Scale Background Error Covariances in 3D-Var Data Assimilation

NASA Astrophysics Data System (ADS)

Zhang, Xubin; Tan, Zhe-Min

2017-04-01

The construction of background error covariances is a key component of three-dimensional variational data assimilation. There are different scale background errors and interactions among them in the numerical weather Prediction. However, the influence of these errors and their interactions cannot be represented in the background error covariances statistics when estimated by the leading methods. So, it is necessary to construct background error covariances influenced by multi-scale interactions among errors. With the NMC method, this article firstly estimates the background error covariances at given model-resolution scales. And then the information of errors whose scales are larger and smaller than the given ones is introduced respectively, using different nesting techniques, to estimate the corresponding covariances. The comparisons of three background error covariances statistics influenced by information of errors at different scales reveal that, the background error variances enhance particularly at large scales and higher levels when introducing the information of larger-scale errors by the lateral boundary condition provided by a lower-resolution model. On the other hand, the variances reduce at medium scales at the higher levels, while those show slight improvement at lower levels in the nested domain, especially at medium and small scales, when introducing the information of smaller-scale errors by nesting a higher-resolution model. In addition, the introduction of information of larger- (smaller-) scale errors leads to larger (smaller) horizontal and vertical correlation scales of background errors. Considering the multivariate correlations, the Ekman coupling increases (decreases) with the information of larger- (smaller-) scale errors included, whereas the geostrophic coupling in free atmosphere weakens in both situations. The three covariances obtained in above work are used in a data assimilation and model forecast system respectively, and then the analysis-forecast cycles for a period of 1 month are conducted. Through the comparison of both analyses and forecasts from this system, it is found that the trends for variation in analysis increments with information of different scale errors introduced are consistent with those for variation in variances and correlations of background errors. In particular, introduction of smaller-scale errors leads to larger amplitude of analysis increments for winds at medium scales at the height of both high- and low- level jet. And analysis increments for both temperature and humidity are greater at the corresponding scales at middle and upper levels under this circumstance. These analysis increments improve the intensity of jet-convection system which includes jets at different levels and coupling between them associated with latent heat release, and these changes in analyses contribute to the better forecasts for winds and temperature in the corresponding areas. When smaller-scale errors are included, analysis increments for humidity enhance significantly at large scales at lower levels to moisten southern analyses. This humidification devotes to correcting dry bias there and eventually improves forecast skill of humidity. Moreover, inclusion of larger- (smaller-) scale errors is beneficial for forecast quality of heavy (light) precipitation at large (small) scales due to the amplification (diminution) of intensity and area in precipitation forecasts but tends to overestimate (underestimate) light (heavy) precipitation .

The Effects of Laser Phase Noise on Laser Radar Performance

DTIC Science & Technology

1992-12-01

Laboratory 5. Figure 3 shows Allan variance plots of the above ultrastable C02 laser which has an open Fabry - Perot cavity 5. The open and solid circles...the same measurement time -r) by more than 10 dB. Therefore, the root Allan variance for the Fabry - Perot cavity ultrastable C02 laser can be...variance so that the SSB phase noise for the Fabry - Perot cavity ultrastable CO 2 laser is about 20 dB (because of the squaring operation) below that of the

A multi-frequency inverse-phase error compensation method for projector nonlinear in 3D shape measurement

NASA Astrophysics Data System (ADS)

Mao, Cuili; Lu, Rongsheng; Liu, Zhijian

2018-07-01

In fringe projection profilometry, the phase errors caused by the nonlinear intensity response of digital projectors needs to be correctly compensated. In this paper, a multi-frequency inverse-phase method is proposed. The theoretical model of periodical phase errors is analyzed. The periodical phase errors can be adaptively compensated in the wrapped maps by using a set of fringe patterns. The compensated phase is then unwrapped with multi-frequency method. Compared with conventional methods, the proposed method can greatly reduce the periodical phase error without calibrating measurement system. Some simulation and experimental results are presented to demonstrate the validity of the proposed approach.

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.

In vivo volumetric depth-resolved vasculature imaging of human limbus and sclera with 1 μm swept source phase-variance optical coherence angiography

NASA Astrophysics Data System (ADS)

Poddar, Raju; Zawadzki, Robert J.; Cortés, Dennis E.; Mannis, Mark J.; Werner, John S.

2015-06-01

We present in vivo volumetric depth-resolved vasculature images of the anterior segment of the human eye acquired with phase-variance based motion contrast using a high-speed (100 kHz, 105 A-scans/s) swept source optical coherence tomography system (SSOCT). High phase stability SSOCT imaging was achieved by using a computationally efficient phase stabilization approach. The human corneo-scleral junction and sclera were imaged with swept source phase-variance optical coherence angiography and compared with slit lamp images from the same eyes of normal subjects. Different features of the rich vascular system in the conjunctiva and episclera were visualized and described. This system can be used as a potential tool for ophthalmological research to determine changes in the outflow system, which may be helpful for identification of abnormalities that lead to glaucoma.

A Monte Carlo Study of Levene's Test of Homogeneity of Variance: Empirical Frequencies of Type I Error in Normal Distributions.

ERIC Educational Resources Information Center

Neel, John H.; Stallings, William M.

An influential statistics test recommends a Levene text for homogeneity of variance. A recent note suggests that Levene's test is upwardly biased for small samples. Another report shows inflated Alpha estimates and low power. Neither study utilized more than two sample sizes. This Monte Carlo study involved sampling from a normal population for…

Weighting by Inverse Variance or by Sample Size in Random-Effects Meta-Analysis

ERIC Educational Resources Information Center

Marin-Martinez, Fulgencio; Sanchez-Meca, Julio

2010-01-01

Most of the statistical procedures in meta-analysis are based on the estimation of average effect sizes from a set of primary studies. The optimal weight for averaging a set of independent effect sizes is the inverse variance of each effect size, but in practice these weights have to be estimated, being affected by sampling error. When assuming a…

The microcomputer scientific software series 3: general linear model--analysis of variance.

Treesearch

Harold M. Rauscher

1985-01-01

A BASIC language set of programs, designed for use on microcomputers, is presented. This set of programs will perform the analysis of variance for any statistical model describing either balanced or unbalanced designs. The program computes and displays the degrees of freedom, Type I sum of squares, and the mean square for the overall model, the error, and each factor...

A Dual Frequency Carrier Phase Error Difference Checking Algorithm for the GNSS Compass.

PubMed

Liu, Shuo; Zhang, Lei; Li, Jian

2016-11-24

The performance of the Global Navigation Satellite System (GNSS) compass is related to the quality of carrier phase measurement. How to process the carrier phase error properly is important to improve the GNSS compass accuracy. In this work, we propose a dual frequency carrier phase error difference checking algorithm for the GNSS compass. The algorithm aims at eliminating large carrier phase error in dual frequency double differenced carrier phase measurement according to the error difference between two frequencies. The advantage of the proposed algorithm is that it does not need additional environment information and has a good performance on multiple large errors compared with previous research. The core of the proposed algorithm is removing the geographical distance from the dual frequency carrier phase measurement, then the carrier phase error is separated and detectable. We generate the Double Differenced Geometry-Free (DDGF) measurement according to the characteristic that the different frequency carrier phase measurements contain the same geometrical distance. Then, we propose the DDGF detection to detect the large carrier phase error difference between two frequencies. The theoretical performance of the proposed DDGF detection is analyzed. An open sky test, a manmade multipath test and an urban vehicle test were carried out to evaluate the performance of the proposed algorithm. The result shows that the proposed DDGF detection is able to detect large error in dual frequency carrier phase measurement by checking the error difference between two frequencies. After the DDGF detection, the accuracy of the baseline vector is improved in the GNSS compass.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

The Ionospheric Scintillation Effects on the BeiDou Signal Receiver

PubMed Central

He, Zhijun; Zhao, Hongbo; Feng, Wenquan

2016-01-01

Irregularities in the Earth’s ionosphere can make the amplitude and phase of radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could affect the performance of the Global Navigation Satellite System (GNSS). Currently, the Multiple Phase Screen (MPS) technique is widely used in solving problems caused by weak and strong scintillations. Considering that Southern China is mainly located in the area where moderate and intense scintillation occur frequently, this paper built a model based on the MPS technique and discussed the scintillation impacts on China’s BeiDou navigation system. By using the BeiDou B1I signal, this paper analyzed the scintillation effects on the receiver, which includes the acquisition and tracking process. For acquisition process, this paper focused on the correlation peak and acquisition probability. For the tracking process, this paper focused on the carrier tracking loop and the code tracking loop. Simulation results show that under high scintillation intensity, the phase fluctuation could be −1.13 ± 0.087 rad to 1.40 ± 0.087 rad and the relative amplitude fluctuation could be −10 dB to 8 dB. As the scintillation intensity increased, the average correlation peak would decrease more than 8%, which could thus degrade acquisition performance. On the other hand, when the signal-to-noise ratio (SNR) is comparatively lower, the influence of strong scintillation on the phase locked loop (PLL) is much higher than that of weak scintillation. As the scintillation becomes more intense, PLL variance could consequently results in an error of more than 2.02 cm in carrier-phase based ranging. In addition, the delay locked loop (DLL) simulation results indicated that the pseudo-range error caused by strong scintillation could be more than 4 m and the consequent impact on positioning accuracy could be more than 6 m. PMID:27834867

Weighted linear regression using D2H and D2 as the independent variables

Treesearch

Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

Crop area estimation based on remotely-sensed data with an accurate but costly subsample

NASA Technical Reports Server (NTRS)

Gunst, R. F.

1983-01-01

Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.

A General Approach to Defining Latent Growth Components

ERIC Educational Resources Information Center

Mayer, Axel; Steyer, Rolf; Mueller, Horst

2012-01-01

We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth…

Effects of sharing information on drug administration errors in pediatric wards: a pre–post intervention study

PubMed Central

Chua, Siew-Siang; Choo, Sim-Mei; Sulaiman, Che Zuraini; Omar, Asma; Thong, Meow-Keong

2017-01-01

Background and purpose Drug administration errors are more likely to reach the patient than other medication errors. The main aim of this study was to determine whether the sharing of information on drug administration errors among health care providers would reduce such problems. Patients and methods This study involved direct, undisguised observations of drug administrations in two pediatric wards of a major teaching hospital in Kuala Lumpur, Malaysia. This study consisted of two phases: Phase 1 (pre-intervention) and Phase 2 (post-intervention). Data were collected by two observers over a 40-day period in both Phase 1 and Phase 2 of the study. Both observers were pharmacy graduates: Observer 1 just completed her undergraduate pharmacy degree, whereas Observer 2 was doing her one-year internship as a provisionally registered pharmacist in the hospital under study. A drug administration error was defined as a discrepancy between the drug regimen received by the patient and that intended by the prescriber and also drug administration procedures that did not follow standard hospital policies and procedures. Results from Phase 1 of the study were analyzed, presented and discussed with the ward staff before commencement of data collection in Phase 2. Results A total of 1,284 and 1,401 doses of drugs were administered in Phase 1 and Phase 2, respectively. The rate of drug administration errors reduced significantly from Phase 1 to Phase 2 (44.3% versus 28.6%, respectively; P<0.001). Logistic regression analysis showed that the adjusted odds of drug administration errors in Phase 1 of the study were almost three times that in Phase 2 (P<0.001). The most common types of errors were incorrect administration technique and incorrect drug preparation. Nasogastric and intravenous routes of drug administration contributed significantly to the rate of drug administration errors. Conclusion This study showed that sharing of the types of errors that had occurred was significantly associated with a reduction in drug administration errors. PMID:28356748

Time-dependent phase error correction using digital waveform synthesis

DOEpatents

Doerry, Armin W.; Buskirk, Stephen

2017-10-10

The various technologies presented herein relate to correcting a time-dependent phase error generated as part of the formation of a radar waveform. A waveform can be pre-distorted to facilitate correction of an error induced into the waveform by a downstream operation/component in a radar system. For example, amplifier power droop effect can engender a time-dependent phase error in a waveform as part of a radar signal generating operation. The error can be quantified and an according complimentary distortion can be applied to the waveform to facilitate negation of the error during the subsequent processing of the waveform. A time domain correction can be applied by a phase error correction look up table incorporated into a waveform phase generator.

A new stratification of mourning dove call-count routes

USGS Publications Warehouse

Blankenship, L.H.; Humphrey, A.B.; MacDonald, D.

1971-01-01

The mourning dove (Zenaidura macroura) call-count survey is a nationwide audio-census of breeding mourning doves. Recent analyses of the call-count routes have utilized a stratification based upon physiographic regions of the United States. An analysis of 5 years of call-count data, based upon stratification using potential natural vegetation, has demonstrated that this uew stratification results in strata with greater homogeneity than the physiographic strata, provides lower error variance, and hence generates greatet precision in the analysis without an increase in call-count routes. Error variance was reduced approximately 30 percent for the contiguous United States. This indicates that future analysis based upon the new stratification will result in an increased ability to detect significant year-to-year changes.

Technical note: Application of the Box-Cox data transformation to animal science experiments.

PubMed

Peltier, M R; Wilcox, C J; Sharp, D C

1998-03-01

In the use of ANOVA for hypothesis testing in animal science experiments, the assumption of homogeneity of errors often is violated because of scale effects and the nature of the measurements. We demonstrate a method for transforming data so that the assumptions of ANOVA are met (or violated to a lesser degree) and apply it in analysis of data from a physiology experiment. Our study examined whether melatonin implantation would affect progesterone secretion in cycling pony mares. Overall treatment variances were greater in the melatonin-treated group, and several common transformation procedures failed. Application of the Box-Cox transformation algorithm reduced the heterogeneity of error and permitted the assumption of equal variance to be met.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains

PubMed Central

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-01-01

Background Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. Objectives We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Methods Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Results Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Conclusions Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. PMID:27193033

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.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

Attentional effects on orientation judgements are dependent on memory consolidation processes.

PubMed

Haskell, Christie; Anderson, Britt

2016-11-01

Are the effects of memory and attention on perception synergistic, antagonistic, or independent? Tested separately, memory and attention have been shown to affect the accuracy of orientation judgements. When multiple stimuli are presented sequentially versus simultaneously, error variance is reduced. When a target is validly cued, precision is increased. What if they are manipulated together? We combined memory and attention manipulations in an orientation judgement task to answer this question. Two circular gratings were presented sequentially or simultaneously. On some trials a brief luminance cue preceded the stimuli. Participants were cued to report the orientation of one of the two gratings by rotating a response grating. We replicated the finding that error variance is reduced on sequential trials. Critically, we found interacting effects of memory and attention. Valid cueing reduced the median, absolute error only when two stimuli appeared together and improved it to the level of performance on uncued sequential trials, whereas invalid cueing always increased error. This effect was not mediated by cue predictiveness; however, predictive cues reduced the standard deviation of the error distribution, whereas nonpredictive cues reduced "guessing". Our results suggest that, when the demand on memory is greater than a single stimulus, attention is a bottom-up process that prioritizes stimuli for consolidation. Thus attention and memory are synergistic.

Analysis of variance to assess statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Joe, Cody; Lee, Colin; Besio, Walter G

2017-07-01

Concentric ring electrodes have shown promise in non-invasive electrophysiological measurement demonstrating their superiority to conventional disc electrodes, in particular, in accuracy of Laplacian estimation. Recently, we have proposed novel variable inter-ring distances concentric ring electrodes. Analytic and finite element method modeling results for linearly increasing distances electrode configurations suggested they may decrease the truncation error resulting in more accurate Laplacian estimates compared to currently used constant inter-ring distances configurations. This study assesses statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes. Full factorial design of analysis of variance was used with one categorical and two numerical factors: the inter-ring distances, the electrode diameter, and the number of concentric rings in the electrode. The response variables were the Relative Error and the Maximum Error of Laplacian estimation computed using a finite element method model for each of the combinations of levels of three factors. Effects of the main factors and their interactions on Relative Error and Maximum Error were assessed and the obtained results suggest that all three factors have statistically significant effects in the model confirming the potential of using inter-ring distances as a means of improving accuracy of Laplacian estimation.

Location tests for biomarker studies: a comparison using simulations for the two-sample case.

PubMed

Scheinhardt, M O; Ziegler, A

2013-01-01

Gene, protein, or metabolite expression levels are often non-normally distributed, heavy tailed and contain outliers. Standard statistical approaches may fail as location tests in this situation. In three Monte-Carlo simulation studies, we aimed at comparing the type I error levels and empirical power of standard location tests and three adaptive tests [O'Gorman, Can J Stat 1997; 25: 269 -279; Keselman et al., Brit J Math Stat Psychol 2007; 60: 267- 293; Szymczak et al., Stat Med 2013; 32: 524 - 537] for a wide range of distributions. We simulated two-sample scenarios using the g-and-k-distribution family to systematically vary tail length and skewness with identical and varying variability between groups. All tests kept the type I error level when groups did not vary in their variability. The standard non-parametric U-test performed well in all simulated scenarios. It was outperformed by the two non-parametric adaptive methods in case of heavy tails or large skewness. Most tests did not keep the type I error level for skewed data in the case of heterogeneous variances. The standard U-test was a powerful and robust location test for most of the simulated scenarios except for very heavy tailed or heavy skewed data, and it is thus to be recommended except for these cases. The non-parametric adaptive tests were powerful for both normal and non-normal distributions under sample variance homogeneity. But when sample variances differed, they did not keep the type I error level. The parametric adaptive test lacks power for skewed and heavy tailed distributions.

Resolving multiple propagation paths in time of flight range cameras using direct and global separation methods

NASA Astrophysics Data System (ADS)

Whyte, Refael; Streeter, Lee; Cree, Michael J.; Dorrington, Adrian A.

2015-11-01

Time of flight (ToF) range cameras illuminate the scene with an amplitude-modulated continuous wave light source and measure the returning modulation envelopes: phase and amplitude. The phase change of the modulation envelope encodes the distance travelled. This technology suffers from measurement errors caused by multiple propagation paths from the light source to the receiving pixel. The multiple paths can be represented as the summation of a direct return, which is the return from the shortest path length, and a global return, which includes all other returns. We develop the use of a sinusoidal pattern from which a closed form solution for the direct and global returns can be computed in nine frames with the constraint that the global return is a spatially lower frequency than the illuminated pattern. In a demonstration on a scene constructed to have strong multipath interference, we find the direct return is not significantly different from the ground truth in 33/136 pixels tested; where for the full-field measurement, it is significantly different for every pixel tested. The variance in the estimated direct phase and amplitude increases by a factor of eight compared with the standard time of flight range camera technique.

A bundle with a preformatted medical order sheet and an introductory course to reduce prescription errors in neonates.

PubMed

Palmero, David; Di Paolo, Ermindo R; Beauport, Lydie; Pannatier, André; Tolsa, Jean-François

2016-01-01

The objective of this study was to assess whether the introduction of a new preformatted medical order sheet coupled with an introductory course affected prescription quality and the frequency of errors during the prescription stage in a neonatal intensive care unit (NICU). Two-phase observational study consisting of two consecutive 4-month phases: pre-intervention (phase 0) and post-intervention (phase I) conducted in an 11-bed NICU in a Swiss university hospital. Interventions consisted of the introduction of a new preformatted medical order sheet with explicit information supplied, coupled with a staff introductory course on appropriate prescription and medication errors. The main outcomes measured were formal aspects of prescription and frequency and nature of prescription errors. Eighty-three and 81 patients were included in phase 0 and phase I, respectively. A total of 505 handwritten prescriptions in phase 0 and 525 in phase I were analysed. The rate of prescription errors decreased significantly from 28.9% in phase 0 to 13.5% in phase I (p < 0.05). Compared with phase 0, dose errors, name confusion and errors in frequency and rate of drug administration decreased in phase I, from 5.4 to 2.7% (p < 0.05), 5.9 to 0.2% (p < 0.05), 3.6 to 0.2% (p < 0.05), and 4.7 to 2.1% (p < 0.05), respectively. The rate of incomplete and ambiguous prescriptions decreased from 44.2 to 25.7 and 8.5 to 3.2% (p < 0.05), respectively. Inexpensive and simple interventions can improve the intelligibility of prescriptions and reduce medication errors. Medication errors are frequent in NICUs and prescription is one of the most critical steps. CPOE reduce prescription errors, but their implementation is not available everywhere. Preformatted medical order sheet coupled with an introductory course decrease medication errors in a NICU. Preformatted medical order sheet is an inexpensive and readily implemented alternative to CPOE.

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

DOE PAGES

Gonçalves, Fabio; Treuhaft, Robert; Law, Beverly; ...

2017-01-07

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertaintymore » that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.« less

Suboptimal schemes for atmospheric data assimilation based on the Kalman filter

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Cohn, Stephen E.

1994-01-01

This work is directed toward approximating the evolution of forecast error covariances for data assimilation. The performance of different algorithms based on simplification of the standard Kalman filter (KF) is studied. These are suboptimal schemes (SOSs) when compared to the KF, which is optimal for linear problems with known statistics. The SOSs considered here are several versions of optimal interpolation (OI), a scheme for height error variance advection, and a simplified KF in which the full height error covariance is advected. To employ a methodology for exact comparison among these schemes, a linear environment is maintained, in which a beta-plane shallow-water model linearized about a constant zonal flow is chosen for the test-bed dynamics. The results show that constructing dynamically balanced forecast error covariances rather than using conventional geostrophically balanced ones is essential for successful performance of any SOS. A posteriori initialization of SOSs to compensate for model - data imbalance sometimes results in poor performance. Instead, properly constructed dynamically balanced forecast error covariances eliminate the need for initialization. When the SOSs studied here make use of dynamically balanced forecast error covariances, the difference among their performances progresses naturally from conventional OI to the KF. In fact, the results suggest that even modest enhancements of OI, such as including an approximate dynamical equation for height error variances while leaving height error correlation structure homogeneous, go a long way toward achieving the performance of the KF, provided that dynamically balanced cross-covariances are constructed and that model errors are accounted for properly. The results indicate that such enhancements are necessary if unconventional data are to have a positive impact.

Systems Engineering Programmatic Estimation Using Technology Variance

NASA Technical Reports Server (NTRS)

Mog, Robert A.

2000-01-01

Unique and innovative system programmatic estimation is conducted using the variance of the packaged technologies. Covariance analysis is performed on the subsystems and components comprising the system of interest. Technological "return" and "variation" parameters are estimated. These parameters are combined with the model error to arrive at a measure of system development stability. The resulting estimates provide valuable information concerning the potential cost growth of the system under development.

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

PubMed Central

Beasley, T. Mark

2013-01-01

Increasing the correlation between the independent variable and the mediator (a coefficient) increases the effect size (ab) for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in a at some point outweighs the increase of the effect size (ab) and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of ab approximates the variance expected from normal theory. Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a. Implications for statistical analysis and applied researchers are discussed. PMID:24954952

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

Phase measurement error in summation of electron holography series.

PubMed

McLeod, Robert A; Bergen, Michael; Malac, Marek

2014-06-01

Off-axis electron holography is a method for the transmission electron microscope (TEM) that measures the electric and magnetic properties of a specimen. The electrostatic and magnetic potentials modulate the electron wavefront phase. The error in measurement of the phase therefore determines the smallest observable changes in electric and magnetic properties. Here we explore the summation of a hologram series to reduce the phase error and thereby improve the sensitivity of electron holography. Summation of hologram series requires independent registration and correction of image drift and phase wavefront drift, the consequences of which are discussed. Optimization of the electro-optical configuration of the TEM for the double biprism configuration is examined. An analytical model of image and phase drift, composed of a combination of linear drift and Brownian random-walk, is derived and experimentally verified. The accuracy of image registration via cross-correlation and phase registration is characterized by simulated hologram series. The model of series summation errors allows the optimization of phase error as a function of exposure time and fringe carrier frequency for a target spatial resolution. An experimental example of hologram series summation is provided on WS2 fullerenes. A metric is provided to measure the object phase error from experimental results and compared to analytical predictions. The ultimate experimental object root-mean-square phase error is 0.006 rad (2π/1050) at a spatial resolution less than 0.615 nm and a total exposure time of 900 s. The ultimate phase error in vacuum adjacent to the specimen is 0.0037 rad (2π/1700). The analytical prediction of phase error differs with the experimental metrics by +7% inside the object and -5% in the vacuum, indicating that the model can provide reliable quantitative predictions. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Phase correction system for automatic focusing of synthetic aperture radar

DOEpatents

Eichel, Paul H.; Ghiglia, Dennis C.; Jakowatz, Jr., Charles V.

1990-01-01

A phase gradient autofocus system for use in synthetic aperture imaging accurately compensates for arbitrary phase errors in each imaged frame by locating highlighted areas and determining the phase disturbance or image spread associated with each of these highlight areas. An estimate of the image spread for each highlighted area in a line in the case of one dimensional processing or in a sector, in the case of two-dimensional processing, is determined. The phase error is determined using phase gradient processing. The phase error is then removed from the uncorrected image and the process is iteratively performed to substantially eliminate phase errors which can degrade the image.

Inventory implications of using sampling variances in estimation of growth model coefficients

Treesearch

Albert R. Stage; William R. Wykoff

2000-01-01

Variables based on stand densities or stocking have sampling errors that depend on the relation of tree size to plot size and on the spatial structure of the population, ignoring the sampling errors of such variables, which include most measures of competition used in both distance-dependent and distance-independent growth models, can bias the predictions obtained from...

POOLMS: A computer program for fitting and model selection for two level factorial replication-free experiments

NASA Technical Reports Server (NTRS)

Amling, G. E.; Holms, A. G.

1973-01-01

A computer program is described that performs a statistical multiple-decision procedure called chain pooling. It uses a number of mean squares assigned to error variance that is conditioned on the relative magnitudes of the mean squares. The model selection is done according to user-specified levels of type 1 or type 2 error probabilities.

Comparison of Efficiency of Jackknife and Variance Component Estimators of Standard Errors. Program Statistics Research. Technical Report.

ERIC Educational Resources Information Center

Longford, Nicholas T.

Large scale surveys usually employ a complex sampling design and as a consequence, no standard methods for estimation of the standard errors associated with the estimates of population means are available. Resampling methods, such as jackknife or bootstrap, are often used, with reference to their properties of robustness and reduction of bias. A…

Projections of Southern Hemisphere atmospheric circulation interannual variability

NASA Astrophysics Data System (ADS)

Grainger, Simon; Frederiksen, Carsten S.; Zheng, Xiaogu

2017-02-01

An analysis is made of the coherent patterns, or modes, of interannual variability of Southern Hemisphere 500 hPa geopotential height field under current and projected climate change scenarios. Using three separate multi-model ensembles (MMEs) of coupled model intercomparison project phase 5 (CMIP5) models, the interannual variability of the seasonal mean is separated into components related to (1) intraseasonal processes; (2) slowly-varying internal dynamics; and (3) the slowly-varying response to external changes in radiative forcing. In the CMIP5 RCP8.5 and RCP4.5 experiments, there is very little change in the twenty-first century in the intraseasonal component modes, related to the Southern annular mode (SAM) and mid-latitude wave processes. The leading three slowly-varying internal component modes are related to SAM, the El Niño-Southern oscillation (ENSO), and the South Pacific wave (SPW). Structural changes in the slow-internal SAM and ENSO modes do not exceed a qualitative estimate of the spatial sampling error, but there is a consistent increase in the ENSO-related variance. Changes in the SPW mode exceed the sampling error threshold, but cannot be further attributed. Changes in the dominant slowly-varying external mode are related to projected changes in radiative forcing. They reflect thermal expansion of the tropical troposphere and associated changes in the Hadley Cell circulation. Changes in the externally-forced associated variance in the RCP8.5 experiment are an order of magnitude greater than for the internal components, indicating that the SH seasonal mean circulation will be even more dominated by a SAM-like annular structure. Across the three MMEs, there is convergence in the projected response in the slow-external component.

Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Barker, W. Howard

2004-07-01

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average broadband radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. McICA's sampling errors are evaluated here using a global climate model (GCM) dataset and a correlated-k distribution (CKD) radiation scheme. Two approaches to reduce McICA's sampling variance are discussed. The first is to simply restrict all of McICA's samples to cloudy regions. This avoids wasting precious few samples on essentially homogeneous clear skies. Clear-sky fluxes need to be computed separately for this approach, but this is usually done in GCMs for diagnostic purposes anyway. Second, accuracy can be improved by repeated sampling, and averaging those CKD terms with large cloud radiative effects. Although this naturally increases computational costs over the standard CKD model, random errors for fluxes and heating rates are reduced by typically 50% to 60%, for the present radiation code, when the total number of samples is increased by 50%. When both variance reduction techniques are applied simultaneously, globally averaged flux and heating rate random errors are reduced by a factor of #3.

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

PubMed

Wang, Li-Pen; Ochoa-Rodríguez, Susana; Simões, Nuno Eduardo; Onof, Christian; Maksimović, Cedo

2013-01-01

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Phase noise optimization in temporal phase-shifting digital holography with partial coherence light sources and its application in quantitative cell imaging.

PubMed

Remmersmann, Christian; Stürwald, Stephan; Kemper, Björn; Langehanenberg, Patrik; von Bally, Gert

2009-03-10

In temporal phase-shifting-based digital holographic microscopy, high-resolution phase contrast imaging requires optimized conditions for hologram recording and phase retrieval. To optimize the phase resolution, for the example of a variable three-step algorithm, a theoretical analysis on statistical errors, digitalization errors, uncorrelated errors, and errors due to a misaligned temporal phase shift is carried out. In a second step the theoretically predicted results are compared to the measured phase noise obtained from comparative experimental investigations with several coherent and partially coherent light sources. Finally, the applicability for noise reduction is demonstrated by quantitative phase contrast imaging of pancreas tumor cells.

The Relationship between Occurrence Timing of Dispensing Errors and Subsequent Danger to Patients under the Situation According to the Classification of Drugs by Efficacy.

PubMed

Tsuji, Toshikazu; Nagata, Kenichiro; Kawashiri, Takehiro; Yamada, Takaaki; Irisa, Toshihiro; Murakami, Yuko; Kanaya, Akiko; Egashira, Nobuaki; Masuda, Satohiro

2016-01-01

There are many reports regarding various medical institutions' attempts at the prevention of dispensing errors. However, the relationship between occurrence timing of dispensing errors and subsequent danger to patients has not been studied under the situation according to the classification of drugs by efficacy. Therefore, we analyzed the relationship between position and time regarding the occurrence of dispensing errors. Furthermore, we investigated the relationship between occurrence timing of them and danger to patients. In this study, dispensing errors and incidents in three categories (drug name errors, drug strength errors, drug count errors) were classified into two groups in terms of its drug efficacy (efficacy similarity (-) group, efficacy similarity (+) group), into three classes in terms of the occurrence timing of dispensing errors (initial phase errors, middle phase errors, final phase errors). Then, the rates of damage shifting from "dispensing errors" to "damage to patients" were compared as an index of danger between two groups and among three classes. Consequently, the rate of damage in "efficacy similarity (-) group" was significantly higher than that in "efficacy similarity (+) group". Furthermore, the rate of damage is the highest in "initial phase errors", the lowest in "final phase errors" among three classes. From the results of this study, it became clear that the earlier the timing of dispensing errors occurs, the more severe the damage to patients becomes.

Error Cost Escalation Through the Project Life Cycle

NASA Technical Reports Server (NTRS)

Stecklein, Jonette M.; Dabney, Jim; Dick, Brandon; Haskins, Bill; Lovell, Randy; Moroney, Gregory

2004-01-01

It is well known that the costs to fix errors increase as the project matures, but how fast do those costs build? A study was performed to determine the relative cost of fixing errors discovered during various phases of a project life cycle. This study used three approaches to determine the relative costs: the bottom-up cost method, the total cost breakdown method, and the top-down hypothetical project method. The approaches and results described in this paper presume development of a hardware/software system having project characteristics similar to those used in the development of a large, complex spacecraft, a military aircraft, or a small communications satellite. The results show the degree to which costs escalate, as errors are discovered and fixed at later and later phases in the project life cycle. If the cost of fixing a requirements error discovered during the requirements phase is defined to be 1 unit, the cost to fix that error if found during the design phase increases to 3 - 8 units; at the manufacturing/build phase, the cost to fix the error is 7 - 16 units; at the integration and test phase, the cost to fix the error becomes 21 - 78 units; and at the operations phase, the cost to fix the requirements error ranged from 29 units to more than 1500 units

Estimation of genetic parameters for milk yield in Murrah buffaloes by Bayesian inference.

PubMed

Breda, F C; Albuquerque, L G; Euclydes, R F; Bignardi, A B; Baldi, F; Torres, R A; Barbosa, L; Tonhati, H

2010-02-01

Random regression models were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Bayesian inference. Data comprised 17,935 test-day milk records from 1,433 buffaloes. Twelve models were tested using different combinations of third-, fourth-, fifth-, sixth-, and seventh-order orthogonal polynomials of weeks of lactation for additive genetic and permanent environmental effects. All models included the fixed effects of contemporary group, number of daily milkings and age of cow at calving as covariate (linear and quadratic effect). In addition, residual variances were considered to be heterogeneous with 6 classes of variance. Models were selected based on the residual mean square error, weighted average of residual variance estimates, and estimates of variance components, heritabilities, correlations, eigenvalues, and eigenfunctions. Results indicated that changes in the order of fit for additive genetic and permanent environmental random effects influenced the estimation of genetic parameters. Heritability estimates ranged from 0.19 to 0.31. Genetic correlation estimates were close to unity between adjacent test-day records, but decreased gradually as the interval between test-days increased. Results from mean squared error and weighted averages of residual variance estimates suggested that a model considering sixth- and seventh-order Legendre polynomials for additive and permanent environmental effects, respectively, and 6 classes for residual variances, provided the best fit. Nevertheless, this model presented the largest degree of complexity. A more parsimonious model, with fourth- and sixth-order polynomials, respectively, for these same effects, yielded very similar genetic parameter estimates. Therefore, this last model is recommended for routine applications. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Undesired variance due to examiner stringency/leniency effect in communication skill scores assessed in OSCEs.

PubMed

Harasym, Peter H; Woloschuk, Wayne; Cunning, Leslie

2008-12-01

Physician-patient communication is a clinical skill that can be learned and has a positive impact on patient satisfaction and health outcomes. A concerted effort at all medical schools is now directed at teaching and evaluating this core skill. Student communication skills are often assessed by an Objective Structure Clinical Examination (OSCE). However, it is unknown what sources of error variance are introduced into examinee communication scores by various OSCE components. This study primarily examined the effect different examiners had on the evaluation of students' communication skills assessed at the end of a family medicine clerkship rotation. The communication performance of clinical clerks from Classes 2005 and 2006 were assessed using six OSCE stations. Performance was rated at each station using the 28-item Calgary-Cambridge guide. Item Response Theory analysis using a Multifaceted Rasch model was used to partition the various sources of error variance and generate a "true" communication score where the effects of examiner, case, and items are removed. Variance and reliability of scores were as follows: communication scores (.20 and .87), examiner stringency/leniency (.86 and .91), case (.03 and .96), and item (.86 and .99), respectively. All facet scores were reliable (.87-.99). Examiner variance (.86) was more than four times the examinee variance (.20). About 11% of the clerks' outcome status shifted using "true" rather than observed/raw scores. There was large variability in examinee scores due to variation in examiner stringency/leniency behaviors that may impact pass-fail decisions. Exploring the benefits of examiner training and employing "true" scores generated using Item Response Theory analyses prior to making pass/fail decisions are recommended.

Behavior of sensitivities in the one-dimensional advection-dispersion equation: Implications for parameter estimation and sampling design

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1987-01-01

The spatial and temporal variability of sensitivities has a significant impact on parameter estimation and sampling design for studies of solute transport in porous media. Physical insight into the behavior of sensitivities is offered through an analysis of analytically derived sensitivities for the one-dimensional form of the advection-dispersion equation. When parameters are estimated in regression models of one-dimensional transport, the spatial and temporal variability in sensitivities influences variance and covariance of parameter estimates. Several principles account for the observed influence of sensitivities on parameter uncertainty. (1) Information about a physical parameter may be most accurately gained at points in space and time with a high sensitivity to the parameter. (2) As the distance of observation points from the upstream boundary increases, maximum sensitivity to velocity during passage of the solute front increases and the consequent estimate of velocity tends to have lower variance. (3) The frequency of sampling must be “in phase” with the S shape of the dispersion sensitivity curve to yield the most information on dispersion. (4) The sensitivity to the dispersion coefficient is usually at least an order of magnitude less than the sensitivity to velocity. (5) The assumed probability distribution of random error in observations of solute concentration determines the form of the sensitivities. (6) If variance in random error in observations is large, trends in sensitivities of observation points may be obscured by noise and thus have limited value in predicting variance in parameter estimates among designs. (7) Designs that minimize the variance of one parameter may not necessarily minimize the variance of other parameters. (8) The time and space interval over which an observation point is sensitive to a given parameter depends on the actual values of the parameters in the underlying physical system.

Validating Variance Similarity Functions in the Entrainment Zone

NASA Astrophysics Data System (ADS)

Osman, M.; Turner, D. D.; Heus, T.; Newsom, R. K.

2017-12-01

In previous work, the water vapor variance in the entrainment zone was proposed to be proportional to the convective velocity scale, gradient water vapor mixing ratio and the Brunt-Vaisala frequency in the interfacial layer, while the variance of the vertical wind at in the entrainment zone was defined in terms of the convective velocity scale. The variances in the entrainment zone have been hypothesized to depend on two distinct functions, which also depend on the Richardson number. To the best of our knowledge, these hypotheses have never been tested observationally. Simultaneous measurements of the Eddy correlation surface flux, wind shear profiles from wind profilers, and variance profile measurements of vertical motions and water vapor by Doppler and Raman lidars, respectively, provide a unique opportunity to thoroughly examine the functions used in defining the variances and validate them. These observations were made over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. We have identified about 30 cases from 2016 during which the convective boundary layer (CBL) is quasi-stationary and well mixed for at least 2 hours. The vertical profiles of turbulent fluctuations of the vertical wind and water vapor have been derived using an auto covariance technique to separate out the instrument random error to a set of 2-h period time series. The error analysis of the lidars observations demonstrates that the lidars are capable of resolving the vertical structure of turbulence around the entrainment zone. Therefore, utilizing this unique combination of observations, this study focuses on extensively testing the hypotheses that the second-order moments are indeed proportional to the functions which also depend on Richardson number. The coefficients that are used in defining the functions will also be determined observationally and compared against with the values suggested by Large eddy simulation (LES) studies.

Study on the influence of stochastic properties of correction terms on the reliability of instantaneous network RTK

NASA Astrophysics Data System (ADS)

Próchniewicz, Dominik

2014-03-01

The reliability of precision GNSS positioning primarily depends on correct carrier-phase ambiguity resolution. An optimal estimation and correct validation of ambiguities necessitates a proper definition of mathematical positioning model. Of particular importance in the model definition is the taking into account of the atmospheric errors (ionospheric and tropospheric refraction) as well as orbital errors. The use of the network of reference stations in kinematic positioning, known as Network-based Real-Time Kinematic (Network RTK) solution, facilitates the modeling of such errors and their incorporation, in the form of correction terms, into the functional description of positioning model. Lowered accuracy of corrections, especially during atmospheric disturbances, results in the occurrence of unaccounted biases, the so-called residual errors. The taking into account of such errors in Network RTK positioning model is possible by incorporating the accuracy characteristics of the correction terms into the stochastic model of observations. In this paper we investigate the impact of the expansion of the stochastic model to include correction term variances on the reliability of the model solution. In particular the results of instantaneous solution that only utilizes a single epoch of GPS observations, is analyzed. Such a solution mode due to the low number of degrees of freedom is very sensitive to an inappropriate mathematical model definition. Thus the high level of the solution reliability is very difficult to achieve. Numerical tests performed for a test network located in mountain area during ionospheric disturbances allows to verify the described method for the poor measurement conditions. The results of the ambiguity resolution as well as the rover positioning accuracy shows that the proposed method of stochastic modeling can increase the reliability of instantaneous Network RTK performance.

An Empirical State Error Covariance Matrix for Batch State Estimation

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2011-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).

Data assimilation method based on the constraints of confidence region

NASA Astrophysics Data System (ADS)

Li, Yong; Li, Siming; Sheng, Yao; Wang, Luheng

2018-03-01

The ensemble Kalman filter (EnKF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence. Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called EnCR, to estimate the inflation parameter of the forecast error variance of the EnKF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods.

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.

Simulation of co-phase error correction of optical multi-aperture imaging system based on stochastic parallel gradient decent algorithm

NASA Astrophysics Data System (ADS)

He, Xiaojun; Ma, Haotong; Luo, Chuanxin

2016-10-01

The optical multi-aperture imaging system is an effective way to magnify the aperture and increase the resolution of telescope optical system, the difficulty of which lies in detecting and correcting of co-phase error. This paper presents a method based on stochastic parallel gradient decent algorithm (SPGD) to correct the co-phase error. Compared with the current method, SPGD method can avoid detecting the co-phase error. This paper analyzed the influence of piston error and tilt error on image quality based on double-aperture imaging system, introduced the basic principle of SPGD algorithm, and discuss the influence of SPGD algorithm's key parameters (the gain coefficient and the disturbance amplitude) on error control performance. The results show that SPGD can efficiently correct the co-phase error. The convergence speed of the SPGD algorithm is improved with the increase of gain coefficient and disturbance amplitude, but the stability of the algorithm reduced. The adaptive gain coefficient can solve this problem appropriately. This paper's results can provide the theoretical reference for the co-phase error correction of the multi-aperture imaging system.

Influence of OPD in wavelength-shifting interferometry

NASA Astrophysics Data System (ADS)

Wang, Hongjun; Tian, Ailing; Liu, Bingcai; Dang, Juanjuan

2009-12-01

Phase-shifting interferometry is a powerful tool for high accuracy optical measurement. It operates by change the optical path length in the reference arm or test arm. This method practices by move optical device. So it has much problem when the optical device is very large and heavy. For solve this problem, the wavelength-shifting interferometry was put forwarded. In wavelength-shifting interferometry, the phase shifting angle was achieved by change the wavelength of optical source. The phase shifting angle was decided by wavelength and OPD (Optical Path Difference) between test and reference wavefront. So the OPD is an important factor to measure results. But in measurement, because the positional error and profile error of under testing optical element is exist, the phase shifting angle is different in different test point when wavelength scanning, it will introduce phase shifting angle error, so it will introduce optical surface measure error. For analysis influence of OPD on optical surface error, the relation between surface error and OPD was researched. By simulation, the relation between phase shifting error and OPD was established. By analysis, the error compensation method was put forward. After error compensation, the measure results can be improved to great extend.

Influence of OPD in wavelength-shifting interferometry

NASA Astrophysics Data System (ADS)

Wang, Hongjun; Tian, Ailing; Liu, Bingcai; Dang, Juanjuan

2010-03-01

Phase-shifting interferometry is a powerful tool for high accuracy optical measurement. It operates by change the optical path length in the reference arm or test arm. This method practices by move optical device. So it has much problem when the optical device is very large and heavy. For solve this problem, the wavelength-shifting interferometry was put forwarded. In wavelength-shifting interferometry, the phase shifting angle was achieved by change the wavelength of optical source. The phase shifting angle was decided by wavelength and OPD (Optical Path Difference) between test and reference wavefront. So the OPD is an important factor to measure results. But in measurement, because the positional error and profile error of under testing optical element is exist, the phase shifting angle is different in different test point when wavelength scanning, it will introduce phase shifting angle error, so it will introduce optical surface measure error. For analysis influence of OPD on optical surface error, the relation between surface error and OPD was researched. By simulation, the relation between phase shifting error and OPD was established. By analysis, the error compensation method was put forward. After error compensation, the measure results can be improved to great extend.

Errors in the Extra-Analytical Phases of Clinical Chemistry Laboratory Testing.

PubMed

Zemlin, Annalise E

2018-04-01

The total testing process consists of various phases from the pre-preanalytical to the post-postanalytical phase, the so-called brain-to-brain loop. With improvements in analytical techniques and efficient quality control programmes, most laboratory errors now occur in the extra-analytical phases. There has been recent interest in these errors with numerous publications highlighting their effect on service delivery, patient care and cost. This interest has led to the formation of various working groups whose mission is to develop standardized quality indicators which can be used to measure the performance of service of these phases. This will eventually lead to the development of external quality assessment schemes to monitor these phases in agreement with ISO15189:2012 recommendations. This review focuses on potential errors in the extra-analytical phases of clinical chemistry laboratory testing, some of the studies performed to assess the severity and impact of these errors and processes that are in place to address these errors. The aim of this review is to highlight the importance of these errors for the requesting clinician.

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.

Daily Goals Formulation and Enhanced Visualization of Mechanical Ventilation Variance Improves Mechanical Ventilation Score.

PubMed

Walsh, Brian K; Smallwood, Craig; Rettig, Jordan; Kacmarek, Robert M; Thompson, John; Arnold, John H

2017-03-01

The systematic implementation of evidence-based practice through the use of guidelines, checklists, and protocols mitigates the risks associated with mechanical ventilation, yet variation in practice remains prevalent. Recent advances in software and hardware have allowed for the development and deployment of an enhanced visualization tool that identifies mechanical ventilation goal variance. Our aim was to assess the utility of daily goal establishment and a computer-aided visualization of variance. This study was composed of 3 phases: a retrospective observational phase (baseline) followed by 2 prospective sequential interventions. Phase I intervention comprised daily goal establishment of mechanical ventilation. Phase II intervention was the setting and monitoring of daily goals of mechanical ventilation with a web-based data visualization system (T3). A single score of mechanical ventilation was developed to evaluate the outcome. The baseline phase evaluated 130 subjects, phase I enrolled 31 subjects, and phase II enrolled 36 subjects. There were no differences in demographic characteristics between cohorts. A total of 171 verbalizations of goals of mechanical ventilation were completed in phase I. The use of T3 increased by 87% from phase I. Mechanical ventilation score improved by 8.4% in phase I and 11.3% in phase II from baseline ( P = .032). The largest effect was in the low risk V T category, with a 40.3% improvement from baseline in phase I, which was maintained at 39% improvement from baseline in phase II ( P = .01). mechanical ventilation score was 9% higher on average in those who survived. Daily goal formation and computer-enhanced visualization of mechanical ventilation variance were associated with an improvement in goal attainment by evidence of an improved mechanical ventilation score. Further research is needed to determine whether improvements in mechanical ventilation score through a targeted, process-oriented intervention will lead to improved patient outcomes. (ClinicalTrials.gov registration NCT02184208.). Copyright © 2017 by Daedalus Enterprises.

Systematic Error Study for ALICE charged-jet v2 Measurement

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

Heinz, M.; Soltz, R.

We study the treatment of systematic errors in the determination of v 2 for charged jets in √ sNN = 2:76 TeV Pb-Pb collisions by the ALICE Collaboration. Working with the reported values and errors for the 0-5% centrality data we evaluate the Χ 2 according to the formulas given for the statistical and systematic errors, where the latter are separated into correlated and shape contributions. We reproduce both the Χ 2 and p-values relative to a null (zero) result. We then re-cast the systematic errors into an equivalent co-variance matrix and obtain identical results, demonstrating that the two methodsmore » are equivalent.« less

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

Gonçalves, Fabio; Treuhaft, Robert; Law, Beverly

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertaintymore » that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.« less

Error-related brain activity predicts cocaine use after treatment at 3-month follow-up.

PubMed

Marhe, Reshmi; van de Wetering, Ben J M; Franken, Ingmar H A

2013-04-15

Relapse after treatment is one of the most important problems in drug dependency. Several studies suggest that lack of cognitive control is one of the causes of relapse. In this study, a relative new electrophysiologic index of cognitive control, the error-related negativity, is investigated to examine its suitability as a predictor of relapse. The error-related negativity was measured in 57 cocaine-dependent patients during their first week in detoxification treatment. Data from 49 participants were used to predict cocaine use at 3-month follow-up. Cocaine use at follow-up was measured by means of self-reported days of cocaine use in the last month verified by urine screening. A multiple hierarchical regression model was used to examine the predictive value of the error-related negativity while controlling for addiction severity and self-reported craving in the week before treatment. The error-related negativity was the only significant predictor in the model and added 7.4% of explained variance to the control variables, resulting in a total of 33.4% explained variance in the prediction of days of cocaine use at follow-up. A reduced error-related negativity measured during the first week of treatment was associated with more days of cocaine use at 3-month follow-up. Moreover, the error-related negativity was a stronger predictor of recent cocaine use than addiction severity and craving. These results suggest that underactive error-related brain activity might help to identify patients who are at risk of relapse as early as in the first week of detoxification treatment. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes?

PubMed

Magnani, Robert J; Ross, John; Williamson, Jessica; Weinberger, Michelle

2018-03-21

The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual mCPR tracking estimates for FP2020. © Magnani et al.

Analytic variance estimates of Swank and Fano factors

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

Gutierrez, Benjamin; Badano, Aldo; Samuelson, Frank, E-mail: frank.samuelson@fda.hhs.gov

Purpose: Variance estimates for detector energy resolution metrics can be used as stopping criteria in Monte Carlo simulations for the purpose of ensuring a small uncertainty of those metrics and for the design of variance reduction techniques. Methods: The authors derive an estimate for the variance of two energy resolution metrics, the Swank factor and the Fano factor, in terms of statistical moments that can be accumulated without significant computational overhead. The authors examine the accuracy of these two estimators and demonstrate how the estimates of the coefficient of variation of the Swank and Fano factors behave with data frommore » a Monte Carlo simulation of an indirect x-ray imaging detector. Results: The authors' analyses suggest that the accuracy of their variance estimators is appropriate for estimating the actual variances of the Swank and Fano factors for a variety of distributions of detector outputs. Conclusions: The variance estimators derived in this work provide a computationally convenient way to estimate the error or coefficient of variation of the Swank and Fano factors during Monte Carlo simulations of radiation imaging systems.« less

An application of the LC-LSTM framework to the self-esteem instability case.

PubMed

Alessandri, Guido; Vecchione, Michele; Donnellan, Brent M; Tisak, John

2013-10-01

The present research evaluates the stability of self-esteem as assessed by a daily version of the Rosenberg (Society and the adolescent self-image, Princeton University Press, Princeton, 1965) general self-esteem scale (RGSE). The scale was administered to 391 undergraduates for five consecutive days. The longitudinal data were analyzed using the integrated LC-LSTM framework that allowed us to evaluate: (1) the measurement invariance of the RGSE, (2) its stability and change across the 5-day assessment period, (3) the amount of variance attributable to stable and transitory latent factors, and (4) the criterion-related validity of these factors. Results provided evidence for measurement invariance, mean-level stability, and rank-order stability of daily self-esteem. Latent state-trait analyses revealed that variances in scores of the RGSE can be decomposed into six components: stable self-esteem (40 %), ephemeral (or temporal-state) variance (36 %), stable negative method variance (9 %), stable positive method variance (4 %), specific variance (1 %) and random error variance (10 %). Moreover, latent factors associated with daily self-esteem were associated with measures of depression, implicit self-esteem, and grade point average.

Adaptive use of research aircraft data sets for hurricane forecasts

NASA Astrophysics Data System (ADS)

Biswas, M. K.; Krishnamurti, T. N.

2008-02-01

This study uses an adaptive observational strategy for hurricane forecasting. It shows the impacts of Lidar Atmospheric Sensing Experiment (LASE) and dropsonde data sets from Convection and Moisture Experiment (CAMEX) field campaigns on hurricane track and intensity forecasts. The following cases are used in this study: Bonnie, Danielle and Georges of 1998 and Erin, Gabrielle and Humberto of 2001. A single model run for each storm is carried out using the Florida State University Global Spectral Model (FSUGSM) with the European Center for Medium Range Weather Forecasts (ECMWF) analysis as initial conditions, in addition to 50 other model runs where the analysis is randomly perturbed for each storm. The centers of maximum variance of the DLM heights are located from the forecast error variance fields at the 84-hr forecast. Back correlations are then performed using the centers of these maximum variances and the fields at the 36-hr forecast. The regions having the highest correlations in the vicinity of the hurricanes are indicative of regions from where the error growth emanates and suggests the need for additional observations. Data sets are next assimilated in those areas that contain high correlations. Forecasts are computed using the new initial conditions for the storm cases, and track and intensity skills are then examined with respect to the control forecast. The adaptive strategy is capable of identifying sensitive areas where additional observations can help in reducing the hurricane track forecast errors. A reduction of position error by approximately 52% for day 3 of forecast (averaged over 7 storm cases) over the control runs is observed. The intensity forecast shows only a slight positive impact due to the model’s coarse resolution.

Comparison of point counts and territory mapping for detecting effects of forest management on songbirds

USGS Publications Warehouse

Newell, Felicity L.; Sheehan, James; Wood, Petra Bohall; Rodewald, Amanda D.; Buehler, David A.; Keyser, Patrick D.; Larkin, Jeffrey L.; Beachy, Tiffany A.; Bakermans, Marja H.; Boves, Than J.; Evans, Andrea; George, Gregory A.; McDermott, Molly E.; Perkins, Kelly A.; White, Matthew; Wigley, T. Bently

2013-01-01

Point counts are commonly used to assess changes in bird abundance, including analytical approaches such as distance sampling that estimate density. Point-count methods have come under increasing scrutiny because effects of detection probability and field error are difficult to quantify. For seven forest songbirds, we compared fixed-radii counts (50 m and 100 m) and density estimates obtained from distance sampling to known numbers of birds determined by territory mapping. We applied point-count analytic approaches to a typical forest management question and compared results to those obtained by territory mapping. We used a before–after control impact (BACI) analysis with a data set collected across seven study areas in the central Appalachians from 2006 to 2010. Using a 50-m fixed radius, variance in error was at least 1.5 times that of the other methods, whereas a 100-m fixed radius underestimated actual density by >3 territories per 10 ha for the most abundant species. Distance sampling improved accuracy and precision compared to fixed-radius counts, although estimates were affected by birds counted outside 10-ha units. In the BACI analysis, territory mapping detected an overall treatment effect for five of the seven species, and effects were generally consistent each year. In contrast, all point-count methods failed to detect two treatment effects due to variance and error in annual estimates. Overall, our results highlight the need for adequate sample sizes to reduce variance, and skilled observers to reduce the level of error in point-count data. Ultimately, the advantages and disadvantages of different survey methods should be considered in the context of overall study design and objectives, allowing for trade-offs among effort, accuracy, and power to detect treatment effects.

Investigation of noise properties in grating-based x-ray phase tomography with reverse projection method

NASA Astrophysics Data System (ADS)

Bao, Yuan; Wang, Yan; Gao, Kun; Wang, Zhi-Li; Zhu, Pei-Ping; Wu, Zi-Yu

2015-10-01

The relationship between noise variance and spatial resolution in grating-based x-ray phase computed tomography (PCT) imaging is investigated with reverse projection extraction method, and the noise variances of the reconstructed absorption coefficient and refractive index decrement are compared. For the differential phase contrast method, the noise variance in the differential projection images follows the same inverse-square law with spatial resolution as in conventional absorption-based x-ray imaging projections. However, both theoretical analysis and simulations demonstrate that in PCT the noise variance of the reconstructed refractive index decrement scales with spatial resolution follows an inverse linear relationship at fixed slice thickness, while the noise variance of the reconstructed absorption coefficient conforms with the inverse cubic law. The results indicate that, for the same noise variance level, PCT imaging may enable higher spatial resolution than conventional absorption computed tomography (ACT), while ACT benefits more from degraded spatial resolution. This could be a useful guidance in imaging the inner structure of the sample in higher spatial resolution. Project supported by the National Basic Research Program of China (Grant No. 2012CB825800), the Science Fund for Creative Research Groups, the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant Nos. KJCX2-YW-N42 and Y4545320Y2), the National Natural Science Foundation of China (Grant Nos. 11475170, 11205157, 11305173, 11205189, 11375225, 11321503, 11179004, and U1332109).

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2015-01-01

Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR+, implements a gating function without pre-specifying a particular region of the patient’s breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR+ algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR+ implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR+ in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR+. The experimental results show that the EKF-GPR+ algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR+ reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR+ controls the duty cycle with reasonable accuracy.

Non-stationary internal tides observed with satellite altimetry

NASA Astrophysics Data System (ADS)

Ray, R. D.; Zaron, E. D.

2011-09-01

Temporal variability of the internal tide is inferred from a 17-year combined record of Topex/Poseidon and Jason satellite altimeters. A global sampling of along-track sea-surface height wavenumber spectra finds that non-stationary variance is generally 25% or less of the average variance at wavenumbers characteristic of mode-1 tidal internal waves. With some exceptions the non-stationary variance does not exceed 0.25 cm2. The mode-2 signal, where detectable, contains a larger fraction of non-stationary variance, typically 50% or more. Temporal subsetting of the data reveals interannual variability barely significant compared with tidal estimation error from 3-year records. Comparison of summer vs. winter conditions shows only one region of noteworthy seasonal changes, the northern South China Sea. Implications for the anticipated SWOT altimeter mission are briefly discussed.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Non-Stationary Internal Tides Observed with Satellite Altimetry

NASA Technical Reports Server (NTRS)

Ray, Richard D.; Zaron, E. D.

2011-01-01

Temporal variability of the internal tide is inferred from a 17-year combined record of Topex/Poseidon and Jason satellite altimeters. A global sampling of along-track sea-surface height wavenumber spectra finds that non-stationary variance is generally 25% or less of the average variance at wavenumbers characteristic of mode-l tidal internal waves. With some exceptions the non-stationary variance does not exceed 0.25 sq cm. The mode-2 signal, where detectable, contains a larger fraction of non-stationary variance, typically 50% or more. Temporal subsetting of the data reveals interannual variability barely significant compared with tidal estimation error from 3-year records. Comparison of summer vs. winter conditions shows only one region of noteworthy seasonal changes, the northern South China Sea. Implications for the anticipated SWOT altimeter mission are briefly discussed.

Two Enhancements of the Logarithmic Least-Squares Method for Analyzing Subjective Comparisons

DTIC Science & Technology

1989-03-25

error term. 1 For this model, the total sum of squares ( SSTO ), defined as n 2 SSTO = E (yi y) i=1 can be partitioned into error and regression sums...of the regression line around the mean value. Mathematically, for the model given by equation A.4, SSTO = SSE + SSR (A.6) A-4 where SSTO is the total...sum of squares (i.e., the variance of the yi’s), SSE is error sum of squares, and SSR is the regression sum of squares. SSTO , SSE, and SSR are given

Effect of non-normality on test statistics for one-way independent groups designs.

PubMed

Cribbie, Robert A; Fiksenbaum, Lisa; Keselman, H J; Wilcox, Rand R

2012-02-01

The data obtained from one-way independent groups designs is typically non-normal in form and rarely equally variable across treatment populations (i.e., population variances are heterogeneous). Consequently, the classical test statistic that is used to assess statistical significance (i.e., the analysis of variance F test) typically provides invalid results (e.g., too many Type I errors, reduced power). For this reason, there has been considerable interest in finding a test statistic that is appropriate under conditions of non-normality and variance heterogeneity. Previously recommended procedures for analysing such data include the James test, the Welch test applied either to the usual least squares estimators of central tendency and variability, or the Welch test with robust estimators (i.e., trimmed means and Winsorized variances). A new statistic proposed by Krishnamoorthy, Lu, and Mathew, intended to deal with heterogeneous variances, though not non-normality, uses a parametric bootstrap procedure. In their investigation of the parametric bootstrap test, the authors examined its operating characteristics under limited conditions and did not compare it to the Welch test based on robust estimators. Thus, we investigated how the parametric bootstrap procedure and a modified parametric bootstrap procedure based on trimmed means perform relative to previously recommended procedures when data are non-normal and heterogeneous. The results indicated that the tests based on trimmed means offer the best Type I error control and power when variances are unequal and at least some of the distribution shapes are non-normal. © 2011 The British Psychological Society.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

PubMed

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-05-01

Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Acquisition, representation, and transfer of models of visuo-motor error

PubMed Central

Zhang, Hang; Kulsa, Mila Kirstie C.; Maloney, Laurence T.

2015-01-01

We examined how human subjects acquire and represent models of visuo-motor error and how they transfer information about visuo-motor error from one task to a closely related one. The experiment consisted of three phases. In the training phase, subjects threw beanbags underhand towards targets displayed on a wall-mounted touch screen. The distribution of their endpoints was a vertically elongated bivariate Gaussian. In the subsequent choice phase, subjects repeatedly chose which of two targets varying in shape and size they would prefer to attempt to hit. Their choices allowed us to investigate their internal models of visuo-motor error distribution, including the coordinate system in which they represented visuo-motor error. In the transfer phase, subjects repeated the choice phase from a different vantage point, the same distance from the screen but with the throwing direction shifted 45°. From the new vantage point, visuo-motor error was effectively expanded horizontally by . We found that subjects incorrectly assumed an isotropic distribution in the choice phase but that the anisotropy they assumed in the transfer phase agreed with an objectively correct transfer. We also found that the coordinate system used in coding two-dimensional visuo-motor error in the choice phase was effectively one-dimensional. PMID:26057549

Multiple indicator cokriging with application to optimal sampling for environmental monitoring

NASA Astrophysics Data System (ADS)

Pardo-Igúzquiza, Eulogio; Dowd, Peter A.

2005-02-01

A probabilistic solution to the problem of spatial interpolation of a variable at an unsampled location consists of estimating the local cumulative distribution function (cdf) of the variable at that location from values measured at neighbouring locations. As this distribution is conditional to the data available at neighbouring locations it incorporates the uncertainty of the value of the variable at the unsampled location. Geostatistics provides a non-parametric solution to such problems via the various forms of indicator kriging. In a least squares sense indicator cokriging is theoretically the best estimator but in practice its use has been inhibited by problems such as an increased number of violations of order relations constraints when compared with simpler forms of indicator kriging. In this paper, we describe a methodology and an accompanying computer program for estimating a vector of indicators by simple indicator cokriging, i.e. simultaneous estimation of the cdf for K different thresholds, {F(u,zk),k=1,…,K}, by solving a unique cokriging system for each location at which an estimate is required. This approach produces a variance-covariance matrix of the estimated vector of indicators which is used to fit a model to the estimated local cdf by logistic regression. This model is used to correct any violations of order relations and automatically ensures that all order relations are satisfied, i.e. the estimated cumulative distribution function, F^(u,zk), is such that: F^(u,zk)∈[0,1],∀zk,andF^(u,zk)⩽F^(u,z)forzk

Some New Results on Grubbs’ Estimators.

DTIC Science & Technology

1983-06-01

8217 ESTIMATORS DENNIS A. BRINDLEY AND RALPH A. BRADLEY* Consider a two-way classification with n rows and r columns and the usual model of analysis of variance...except that the error components of the model may have heterogeneous variances, by columns. -Grubbs provided unbiased estimators Q. of a . that depend...of observations yij, i = 1, ... , n, j 1, ... , r, and the model , Yij = Ili + ij + Ej, (1) when Vi represents the mean response of row i, . represents

Performance of Language-Coordinated Collective Systems: A Study of Wine Recognition and Description

PubMed Central

Zubek, Julian; Denkiewicz, Michał; Dębska, Agnieszka; Radkowska, Alicja; Komorowska-Mach, Joanna; Litwin, Piotr; Stępień, Magdalena; Kucińska, Adrianna; Sitarska, Ewa; Komorowska, Krystyna; Fusaroli, Riccardo; Tylén, Kristian; Rączaszek-Leonardi, Joanna

2016-01-01

Most of our perceptions of and engagements with the world are shaped by our immersion in social interactions, cultural traditions, tools and linguistic categories. In this study we experimentally investigate the impact of two types of language-based coordination on the recognition and description of complex sensory stimuli: that of red wine. Participants were asked to taste, remember and successively recognize samples of wines within a larger set in a two-by-two experimental design: (1) either individually or in pairs, and (2) with or without the support of a sommelier card—a cultural linguistic tool designed for wine description. Both effectiveness of recognition and the kinds of errors in the four conditions were analyzed. While our experimental manipulations did not impact recognition accuracy, bias-variance decomposition of error revealed non-trivial differences in how participants solved the task. Pairs generally displayed reduced bias and increased variance compared to individuals, however the variance dropped significantly when they used the sommelier card. The effect of sommelier card reducing the variance was observed only in pairs, individuals did not seem to benefit from the cultural linguistic tool. Analysis of descriptions generated with the aid of sommelier cards shows that pairs were more coherent and discriminative than individuals. The findings are discussed in terms of global properties and dynamics of collective systems when constrained by different types of cultural practices. PMID:27729875

Relationships between genotype x environment interactions and rank orders for a set of genotypes tested in different environments.

PubMed

Hühn, M; Lotito, S; Piepho, H P

1993-09-01

Multilocation trials in plant breeding lead to cross-classified data sets with rows=genotypes and columns=environments, where the breeder is particularly interested in the rank orders of the genotypes in the different environments. Non-identical rank orders are the result of genotype x environment interactions. Not every interaction, however, causes rank changes among the genotypes (rank-interaction). From a breeder's point of view, interaction is tolerable only as long as it does not affect the rank orders. Therefore, the question arises of under which circumstances does interaction become rank-interaction. This paper contributes to our understanding of this topic. In our study we emphasized the detection of relationships between the similarity of the rank orders (measured by Kendall's coefficient of concordance W) and the functions of the diverse variance components (genotypes, environments, interaction, error). On the basis of extensive data sets on different agricultural crops (faba bean, fodder beet, sugar beet, oats, winter rape) obtained from registration trials (1985-1989) carried out in the Federal Republic of Germany, we obtained the following as main result: W ≅ σ 2 (g) /(σ 2 (g) + σ 2 (v) ) where σ 2 (g) =genotypic variance and σ 2 (v) = σ 2 (ge) + σ 2 (o) /L with σ 2 (ge) =interaction variance, σ 2 (o) =error variance and L=number of replications.

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.

Analysis on optical heterodyne frequency error of full-field heterodyne interferometer

NASA Astrophysics Data System (ADS)

Li, Yang; Zhang, Wenxi; Wu, Zhou; Lv, Xiaoyu; Kong, Xinxin; Guo, Xiaoli

2017-06-01

The full-field heterodyne interferometric measurement technology is beginning better applied by employing low frequency heterodyne acousto-optical modulators instead of complex electro-mechanical scanning devices. The optical element surface could be directly acquired by synchronously detecting the received signal phases of each pixel, because standard matrix detector as CCD and CMOS cameras could be used in heterodyne interferometer. Instead of the traditional four-step phase shifting phase calculating, Fourier spectral analysis method is used for phase extracting which brings lower sensitivity to sources of uncertainty and higher measurement accuracy. In this paper, two types of full-field heterodyne interferometer are described whose advantages and disadvantages are also specified. Heterodyne interferometer has to combine two different frequency beams to produce interference, which brings a variety of optical heterodyne frequency errors. Frequency mixing error and beat frequency error are two different kinds of inescapable heterodyne frequency errors. In this paper, the effects of frequency mixing error to surface measurement are derived. The relationship between the phase extraction accuracy and the errors are calculated. :: The tolerance of the extinction ratio of polarization splitting prism and the signal-to-noise ratio of stray light is given. The error of phase extraction by Fourier analysis that caused by beat frequency shifting is derived and calculated. We also propose an improved phase extraction method based on spectrum correction. An amplitude ratio spectrum correction algorithm with using Hanning window is used to correct the heterodyne signal phase extraction. The simulation results show that this method can effectively suppress the degradation of phase extracting caused by beat frequency error and reduce the measurement uncertainty of full-field heterodyne interferometer.

Application of adaptive Kalman filter in vehicle laser Doppler velocimetry

NASA Astrophysics Data System (ADS)

Fan, Zhe; Sun, Qiao; Du, Lei; Bai, Jie; Liu, Jingyun

2018-03-01

Due to the variation of road conditions and motor characteristics of vehicle, great root-mean-square (rms) error and outliers would be caused. Application of Kalman filter in laser Doppler velocimetry(LDV) is important to improve the velocity measurement accuracy. In this paper, the state-space model is built by using current statistical model. A strategy containing two steps is adopted to make the filter adaptive and robust. First, the acceleration variance is adaptively adjusted by using the difference of predictive observation and measured observation. Second, the outliers would be identified and the measured noise variance would be adjusted according to the orthogonal property of innovation to reduce the impaction of outliers. The laboratory rotating table experiments show that adaptive Kalman filter greatly reduces the rms error from 0.59 cm/s to 0.22 cm/s and has eliminated all the outliers. Road experiments compared with a microwave radar show that the rms error of LDV is 0.0218 m/s, and it proves that the adaptive Kalman filtering is suitable for vehicle speed signal processing.

Power analysis to detect treatment effect in longitudinal studies with heterogeneous errors and incomplete data.

PubMed

Vallejo, Guillermo; Ato, Manuel; Fernández García, Paula; Livacic Rojas, Pablo E; Tuero Herrero, Ellián

2016-08-01

 S. Usami (2014) describes a method to realistically determine sample size in longitudinal research using a multilevel model. The present research extends the aforementioned work to situations where it is likely that the assumption of homogeneity of the errors across groups is not met and the error term does not follow a scaled identity covariance structure.   For this purpose, we followed a procedure based on transforming the variance components of the linear growth model and the parameter related to the treatment effect into specific and easily understandable indices. At the same time, we provide the appropriate statistical machinery for researchers to use when data loss is unavoidable, and changes in the expected value of the observed responses are not linear.   The empirical powers based on unknown variance components were virtually the same as the theoretical powers derived from the use of statistically processed indexes.   The main conclusion of the study is the accuracy of the proposed method to calculate sample size in the described situations with the stipulated power criteria.

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

2013-01-01

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Motion-induced error reduction by combining Fourier transform profilometry with phase-shifting profilometry.

PubMed

Li, Beiwen; Liu, Ziping; Zhang, Song

2016-10-03

We propose a hybrid computational framework to reduce motion-induced measurement error by combining the Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). The proposed method is composed of three major steps: Step 1 is to extract continuous relative phase maps for each isolated object with single-shot FTP method and spatial phase unwrapping; Step 2 is to obtain an absolute phase map of the entire scene using PSP method, albeit motion-induced errors exist on the extracted absolute phase map; and Step 3 is to shift the continuous relative phase maps from Step 1 to generate final absolute phase maps for each isolated object by referring to the absolute phase map with error from Step 2. Experiments demonstrate the success of the proposed computational framework for measuring multiple isolated rapidly moving objects.

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.

Decisions to shoot in a weapon identification task: The influence of cultural stereotypes and perceived threat on false positive errors.

PubMed

Fleming, Kevin K; Bandy, Carole L; Kimble, Matthew O

2010-01-01

The decision to shoot a gun engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC), where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and electroencephalogram (EEG) activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of Middle-Eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERNs were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERNs, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of Middle-Eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to Middle-Eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance.

Accounting for measurement error: a critical but often overlooked process.

PubMed

Harris, Edward F; Smith, Richard N

2009-12-01

Due to instrument imprecision and human inconsistencies, measurements are not free of error. Technical error of measurement (TEM) is the variability encountered between dimensions when the same specimens are measured at multiple sessions. A goal of a data collection regimen is to minimise TEM. The few studies that actually quantify TEM, regardless of discipline, report that it is substantial and can affect results and inferences. This paper reviews some statistical approaches for identifying and controlling TEM. Statistically, TEM is part of the residual ('unexplained') variance in a statistical test, so accounting for TEM, which requires repeated measurements, enhances the chances of finding a statistically significant difference if one exists. The aim of this paper was to review and discuss common statistical designs relating to types of error and statistical approaches to error accountability. This paper addresses issues of landmark location, validity, technical and systematic error, analysis of variance, scaled measures and correlation coefficients in order to guide the reader towards correct identification of true experimental differences. Researchers commonly infer characteristics about populations from comparatively restricted study samples. Most inferences are statistical and, aside from concerns about adequate accounting for known sources of variation with the research design, an important source of variability is measurement error. Variability in locating landmarks that define variables is obvious in odontometrics, cephalometrics and anthropometry, but the same concerns about measurement accuracy and precision extend to all disciplines. With increasing accessibility to computer-assisted methods of data collection, the ease of incorporating repeated measures into statistical designs has improved. Accounting for this technical source of variation increases the chance of finding biologically true differences when they exist.

Decisions to Shoot in a Weapon Identification Task: The Influence of Cultural Stereotypes and Perceived Threat on False Positive Errors

PubMed Central

Fleming, Kevin K.; Bandy, Carole L.; Kimble, Matthew O.

2014-01-01

The decision to shoot engages executive control processes that can be biased by cultural stereotypes and perceived threat. The neural locus of the decision to shoot is likely to be found in the anterior cingulate cortex (ACC) where cognition and affect converge. Male military cadets at Norwich University (N=37) performed a weapon identification task in which they made rapid decisions to shoot when images of guns appeared briefly on a computer screen. Reaction times, error rates, and EEG activity were recorded. Cadets reacted more quickly and accurately when guns were primed by images of middle-eastern males wearing traditional clothing. However, cadets also made more false positive errors when tools were primed by these images. Error-related negativity (ERN) was measured for each response. Deeper ERN’s were found in the medial-frontal cortex following false positive responses. Cadets who made fewer errors also produced deeper ERN’s, indicating stronger executive control. Pupil size was used to measure autonomic arousal related to perceived threat. Images of middle-eastern males in traditional clothing produced larger pupil sizes. An image of Osama bin Laden induced the largest pupil size, as would be predicted for the exemplar of Middle East terrorism. Cadets who showed greater increases in pupil size also made more false positive errors. Regression analyses were performed to evaluate predictions based on current models of perceived threat, stereotype activation, and cognitive control. Measures of pupil size (perceived threat) and ERN (cognitive control) explained significant proportions of the variance in false positive errors to middle-eastern males in traditional clothing, while measures of reaction time, signal detection response bias, and stimulus discriminability explained most of the remaining variance. PMID:19813139

A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.

PubMed

Quinchia, Alex G; Falco, Gianluca; Falletti, Emanuela; Dovis, Fabio; Ferrer, Carles

2013-07-24

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

PubMed Central

Quinchia, Alex G.; Falco, Gianluca; Falletti, Emanuela; Dovis, Fabio; Ferrer, Carles

2013-01-01

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways. PMID:23887084

Effect of initial phase on error in electron energy obtained using paraxial approximation for a focused laser pulse in vacuum

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

Singh, Kunwar Pal, E-mail: k-psingh@yahoo.com; Department of Physics, Shri Venkateshwara University, Gajraula, Amroha, Uttar Pradesh 244236; Arya, Rashmi

2015-09-14

We have investigated the effect of initial phase on error in electron energy obtained using paraxial approximation to study electron acceleration by a focused laser pulse in vacuum using a three dimensional test-particle simulation code. The error is obtained by comparing the energy of the electron for paraxial approximation and seventh-order correction description of the fields of Gaussian laser. The paraxial approximation predicts wrong laser divergence and wrong electron escape time from the pulse which leads to prediction of higher energy. The error shows strong phase dependence for the electrons lying along the axis of the laser for linearly polarizedmore » laser pulse. The relative error may be significant for some specific values of initial phase even at moderate values of laser spot sizes. The error does not show initial phase dependence for a circularly laser pulse.« less

Label-free imaging of developing vasculature in zebrafish with phase variance optical coherence microscopy

NASA Astrophysics Data System (ADS)

Chen, Yu; Fingler, Jeff; Trinh, Le A.; Fraser, Scott E.

2016-03-01

A phase variance optical coherence microscope (pvOCM) has been created to visualize blood flow in the vasculature of zebrafish embryos, without using exogenous labels. The pvOCM imaging system has axial and lateral resolutions of 2 μm in tissue, and imaging depth of more than 100 μm. Imaging of 2-5 days post-fertilization zebrafish embryos identified the detailed structures of somites, spinal cord, gut and notochord based on intensity contrast. Visualization of the blood flow in the aorta, veins and intersegmental vessels was achieved with phase variance contrast. The pvOCM vasculature images were confirmed with corresponding fluorescence microscopy of a zebrafish transgene that labels the vasculature with green fluorescent protein. The pvOCM images also revealed functional information of the blood flow activities that is crucial for the study of vascular development.

Research on calibration error of carrier phase against antenna arraying

NASA Astrophysics Data System (ADS)

Sun, Ke; Hou, Xiaomin

2016-11-01

It is the technical difficulty of uplink antenna arraying that signals from various quarters can not be automatically aligned at the target in deep space. The size of the far-field power combining gain is directly determined by the accuracy of carrier phase calibration. It is necessary to analyze the entire arraying system in order to improve the accuracy of the phase calibration. This paper analyzes the factors affecting the calibration error of carrier phase of uplink antenna arraying system including the error of phase measurement and equipment, the error of the uplink channel phase shift, the position error of ground antenna, calibration receiver and target spacecraft, the error of the atmospheric turbulence disturbance. Discuss the spatial and temporal autocorrelation model of atmospheric disturbances. Each antenna of the uplink antenna arraying is no common reference signal for continuous calibration. So it must be a system of the periodic calibration. Calibration is refered to communication of one or more spacecrafts in a certain period. Because the deep space targets are not automatically aligned to multiplexing received signal. Therefore the aligned signal should be done in advance on the ground. Data is shown that the error can be controlled within the range of demand by the use of existing technology to meet the accuracy of carrier phase calibration. The total error can be controlled within a reasonable range.

Optical Pattern Recognition for Missile Guidance.

DTIC Science & Technology

1979-10-01

to the voltage dependent sensitometry noted earlier, to the low lIE intensity available and to the broadband nature of the XE source used. Erase...same form as measures na ri pi vie, crereas Fig. 6) that was used to control the modulator. measures, namely, carrier period variance , carrier phase...This equalizing correlator system is another method modulation or phase variance , and instantaneous fre- by which the flexibility and repertoire of

A novel hybrid scattering order-dependent variance reduction method for Monte Carlo simulations of radiative transfer in cloudy atmosphere

NASA Astrophysics Data System (ADS)

Wang, Zhen; Cui, Shengcheng; Yang, Jun; Gao, Haiyang; Liu, Chao; Zhang, Zhibo

2017-03-01

We present a novel hybrid scattering order-dependent variance reduction method to accelerate the convergence rate in both forward and backward Monte Carlo radiative transfer simulations involving highly forward-peaked scattering phase function. This method is built upon a newly developed theoretical framework that not only unifies both forward and backward radiative transfer in scattering-order-dependent integral equation, but also generalizes the variance reduction formalism in a wide range of simulation scenarios. In previous studies, variance reduction is achieved either by using the scattering phase function forward truncation technique or the target directional importance sampling technique. Our method combines both of them. A novel feature of our method is that all the tuning parameters used for phase function truncation and importance sampling techniques at each order of scattering are automatically optimized by the scattering order-dependent numerical evaluation experiments. To make such experiments feasible, we present a new scattering order sampling algorithm by remodeling integral radiative transfer kernel for the phase function truncation method. The presented method has been implemented in our Multiple-Scaling-based Cloudy Atmospheric Radiative Transfer (MSCART) model for validation and evaluation. The main advantage of the method is that it greatly improves the trade-off between numerical efficiency and accuracy order by order.

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Phase correction and error estimation in InSAR time series analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Fattahi, H.; Amelung, F.

2017-12-01

During the last decade several InSAR time series approaches have been developed in response to the non-idea acquisition strategy of SAR satellites, such as large spatial and temporal baseline with non-regular acquisitions. The small baseline tubes and regular acquisitions of new SAR satellites such as Sentinel-1 allows us to form fully connected networks of interferograms and simplifies the time series analysis into a weighted least square inversion of an over-determined system. Such robust inversion allows us to focus more on the understanding of different components in InSAR time-series and its uncertainties. We present an open-source python-based package for InSAR time series analysis, called PySAR (https://yunjunz.github.io/PySAR/), with unique functionalities for obtaining unbiased ground displacement time-series, geometrical and atmospheric correction of InSAR data and quantifying the InSAR uncertainty. Our implemented strategy contains several features including: 1) improved spatial coverage using coherence-based network of interferograms, 2) unwrapping error correction using phase closure or bridging, 3) tropospheric delay correction using weather models and empirical approaches, 4) DEM error correction, 5) optimal selection of reference date and automatic outlier detection, 6) InSAR uncertainty due to the residual tropospheric delay, decorrelation and residual DEM error, and 7) variance-covariance matrix of final products for geodetic inversion. We demonstrate the performance using SAR datasets acquired by Cosmo-Skymed and TerraSAR-X, Sentinel-1 and ALOS/ALOS-2, with application on the highly non-linear volcanic deformation in Japan and Ecuador (figure 1). Our result shows precursory deformation before the 2015 eruptions of Cotopaxi volcano, with a maximum uplift of 3.4 cm on the western flank (fig. 1b), with a standard deviation of 0.9 cm (fig. 1a), supporting the finding by Morales-Rivera et al. (2017, GRL); and a post-eruptive subsidence on the same area, with a maximum of -3 +/- 0.9 cm (fig. 1c). Time-series displacement map (fig. 2) shows a highly non-linear deformation behavior, indicating the complicated magma propagation process during this eruption cycle.

Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

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.

Analysis and application of minimum variance discrete time system identification

NASA Technical Reports Server (NTRS)

Kaufman, H.; Kotob, S.

1975-01-01

An on-line minimum variance parameter identifier is developed which embodies both accuracy and computational efficiency. The formulation results in a linear estimation problem with both additive and multiplicative noise. The resulting filter which utilizes both the covariance of the parameter vector itself and the covariance of the error in identification is proven to be mean square convergent and mean square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm.

Blind phase error suppression for color-encoded digital fringe projection profilometry

NASA Astrophysics Data System (ADS)

Ma, S.; Zhu, R.; Quan, C.; Li, B.; Tay, C. J.; Chen, L.

2012-04-01

Color-encoded digital fringe projection profilometry (CDFPP) has the advantage of fast speed, non-contact and full-field testing. It is one of the most important dynamic three-dimensional (3D) profile measurement techniques. However, due to factors such as color cross-talk and gamma distortion of electro-optical devices, phase errors arise when conventional phase-shifting algorithms with fixed phase shift values are utilized to retrieve phases. In this paper, a simple and effective blind phase error suppression approach based on isotropic n-dimensional fringe pattern normalization (INFPN) and carrier squeezing interferometry (CSI) is proposed. It does not require pre-calibration for the gamma and color-coupling coefficients or the phase shift values. Simulation and experimental works show that our proposed approach is able to effectively suppress phase errors and achieve accurate measurement results in CDFPP.

3D measurement using combined Gray code and dual-frequency phase-shifting approach

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Liu, Xin

2018-04-01

The combined Gray code and phase-shifting approach is a commonly used 3D measurement technique. In this technique, an error that equals integer multiples of the phase-shifted fringe period, i.e. period jump error, often exists in the absolute analog code, which can lead to gross measurement errors. To overcome this problem, the present paper proposes 3D measurement using a combined Gray code and dual-frequency phase-shifting approach. Based on 3D measurement using the combined Gray code and phase-shifting approach, one set of low-frequency phase-shifted fringe patterns with an odd-numbered multiple of the original phase-shifted fringe period is added. Thus, the absolute analog code measured value can be obtained by the combined Gray code and phase-shifting approach, and the low-frequency absolute analog code measured value can also be obtained by adding low-frequency phase-shifted fringe patterns. Then, the corrected absolute analog code measured value can be obtained by correcting the former by the latter, and the period jump errors can be eliminated, resulting in reliable analog code unwrapping. For the proposed approach, we established its measurement model, analyzed its measurement principle, expounded the mechanism of eliminating period jump errors by error analysis, and determined its applicable conditions. Theoretical analysis and experimental results show that the proposed approach can effectively eliminate period jump errors, reliably perform analog code unwrapping, and improve the measurement accuracy.

Robust dynamic 3-D measurements with motion-compensated phase-shifting profilometry

NASA Astrophysics Data System (ADS)

Feng, Shijie; Zuo, Chao; Tao, Tianyang; Hu, Yan; Zhang, Minliang; Chen, Qian; Gu, Guohua

2018-04-01

Phase-shifting profilometry (PSP) is a widely used approach to high-accuracy three-dimensional shape measurements. However, when it comes to moving objects, phase errors induced by the movement often result in severe artifacts even though a high-speed camera is in use. From our observations, there are three kinds of motion artifacts: motion ripples, motion-induced phase unwrapping errors, and motion outliers. We present a novel motion-compensated PSP to remove the artifacts for dynamic measurements of rigid objects. The phase error of motion ripples is analyzed for the N-step phase-shifting algorithm and is compensated using the statistical nature of the fringes. The phase unwrapping errors are corrected exploiting adjacent reliable pixels, and the outliers are removed by comparing the original phase map with a smoothed phase map. Compared with the three-step PSP, our method can improve the accuracy by more than 95% for objects in motion.

Detection of gene-environment interaction in pedigree data using genome-wide genotypes.

PubMed

Nivard, Michel G; Middeldorp, Christel M; Lubke, Gitta; Hottenga, Jouke-Jan; Abdellaoui, Abdel; Boomsma, Dorret I; Dolan, Conor V

2016-12-01

Heritability may be estimated using phenotypic data collected in relatives or in distantly related individuals using genome-wide single nucleotide polymorphism (SNP) data. We combined these approaches by re-parameterizing the model proposed by Zaitlen et al and extended this model to include moderation of (total and SNP-based) genetic and environmental variance components by a measured moderator. By means of data simulation, we demonstrated that the type 1 error rates of the proposed test are correct and parameter estimates are accurate. As an application, we considered the moderation by age or year of birth of variance components associated with body mass index (BMI), height, attention problems (AP), and symptoms of anxiety and depression. The genetic variance of BMI was found to increase with age, but the environmental variance displayed a greater increase with age, resulting in a proportional decrease of the heritability of BMI. Environmental variance of height increased with year of birth. The environmental variance of AP increased with age. These results illustrate the assessment of moderation of environmental and genetic effects, when estimating heritability from combined SNP and family data. The assessment of moderation of genetic and environmental variance will enhance our understanding of the genetic architecture of complex traits.

Family members' unique perspectives of the family: examining their scope, size, and relations to individual adjustment.

PubMed

Jager, Justin; Bornstein, Marc H; Putnick, Diane L; Hendricks, Charlene

2012-06-01

Using the McMaster Family Assessment Device (Epstein, Baldwin, & Bishop, 1983) and incorporating the perspectives of adolescent, mother, and father, this study examined each family member's "unique perspective" or nonshared, idiosyncratic view of the family. We used a modified multitrait-multimethod confirmatory factor analysis that (a) isolated for each family member's 6 reports of family dysfunction the nonshared variance (a combination of variance idiosyncratic to the individual and measurement error) from variance shared by 1 or more family members and (b) extracted common variance across each family member's set of nonshared variances. The sample included 128 families from a U.S. East Coast metropolitan area. Each family member's unique perspective generalized across his or her different reports of family dysfunction and accounted for a sizable proportion of his or her own variance in reports of family dysfunction. In addition, after holding level of dysfunction constant across families and controlling for a family's shared variance (agreement regarding family dysfunction), each family member's unique perspective was associated with his or her own adjustment. Future applications and competing alternatives for what these "unique perspectives" reflect about the family are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Family Members' Unique Perspectives of the Family: Examining their Scope, Size, and Relations to Individual Adjustment

PubMed Central

Jager, Justin; Bornstein, Marc H.; Diane, L. Putnick; Hendricks, Charlene

2012-01-01

Using the Family Assessment Device (FAD; Epstein, Baldwin, & Bishop, 1983) and incorporating the perspectives of adolescent, mother, and father, this study examined each family member's “unique perspective” or non-shared, idiosyncratic view of the family. To do so we used a modified multitrait-multimethod confirmatory factor analysis that (1) isolated for each family member's six reports of family dysfunction the non-shared variance (a combination of variance idiosyncratic to the individual and measurement error) from variance shared by one or more family members and (2) extracted common variance across each family member's set of non-shared variances. The sample included 128 families from a U.S. East Coast metropolitan area. Each family member's unique perspective generalized across his or her different reports of family dysfunction and accounted for a sizable proportion of his or her own variance in reports of family dysfunction. Additionally, after holding level of dysfunction constant across families and controlling for a family's shared variance (agreement regarding family dysfunction), each family member's unique perspective was associated with his or her own adjustment. Future applications and competing alternatives for what these “unique perspectives” reflect about the family are discussed. PMID:22545933

The effect of covariate mean differences on the standard error and confidence interval for the comparison of treatment means.

PubMed

Liu, Xiaofeng Steven

2011-05-01

The use of covariates is commonly believed to reduce the unexplained error variance and the standard error for the comparison of treatment means, but the reduction in the standard error is neither guaranteed nor uniform over different sample sizes. The covariate mean differences between the treatment conditions can inflate the standard error of the covariate-adjusted mean difference and can actually produce a larger standard error for the adjusted mean difference than that for the unadjusted mean difference. When the covariate observations are conceived of as randomly varying from one study to another, the covariate mean differences can be related to a Hotelling's T(2) . Using this Hotelling's T(2) statistic, one can always find a minimum sample size to achieve a high probability of reducing the standard error and confidence interval width for the adjusted mean difference. ©2010 The British Psychological Society.

Variance reduction for Fokker–Planck based particle Monte Carlo schemes

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

Gorji, M. Hossein, E-mail: gorjih@ifd.mavt.ethz.ch; Andric, Nemanja; Jenny, Patrick

Recently, Fokker–Planck based particle Monte Carlo schemes have been proposed and evaluated for simulations of rarefied gas flows [1–3]. In this paper, the variance reduction for particle Monte Carlo simulations based on the Fokker–Planck model is considered. First, deviational based schemes were derived and reviewed, and it is shown that these deviational methods are not appropriate for practical Fokker–Planck based rarefied gas flow simulations. This is due to the fact that the deviational schemes considered in this study lead either to instabilities in the case of two-weight methods or to large statistical errors if the direct sampling method is applied.more » Motivated by this conclusion, we developed a novel scheme based on correlated stochastic processes. The main idea here is to synthesize an additional stochastic process with a known solution, which is simultaneously solved together with the main one. By correlating the two processes, the statistical errors can dramatically be reduced; especially for low Mach numbers. To assess the methods, homogeneous relaxation, planar Couette and lid-driven cavity flows were considered. For these test cases, it could be demonstrated that variance reduction based on parallel processes is very robust and effective.« less

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

NASA Technical Reports Server (NTRS)

Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

2012-01-01

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Big Data Challenges of High-Dimensional Continuous-Time Mean-Variance Portfolio Selection and a Remedy.

PubMed

Chiu, Mei Choi; Pun, Chi Seng; Wong, Hoi Ying

2017-08-01

Investors interested in the global financial market must analyze financial securities internationally. Making an optimal global investment decision involves processing a huge amount of data for a high-dimensional portfolio. This article investigates the big data challenges of two mean-variance optimal portfolios: continuous-time precommitment and constant-rebalancing strategies. We show that both optimized portfolios implemented with the traditional sample estimates converge to the worst performing portfolio when the portfolio size becomes large. The crux of the problem is the estimation error accumulated from the huge dimension of stock data. We then propose a linear programming optimal (LPO) portfolio framework, which applies a constrained ℓ 1 minimization to the theoretical optimal control to mitigate the risk associated with the dimensionality issue. The resulting portfolio becomes a sparse portfolio that selects stocks with a data-driven procedure and hence offers a stable mean-variance portfolio in practice. When the number of observations becomes large, the LPO portfolio converges to the oracle optimal portfolio, which is free of estimation error, even though the number of stocks grows faster than the number of observations. Our numerical and empirical studies demonstrate the superiority of the proposed approach. © 2017 Society for Risk Analysis.

Regression-assisted deconvolution.

PubMed

McIntyre, Julie; Stefanski, Leonard A

2011-06-30

We present a semi-parametric deconvolution estimator for the density function of a random variable biX that is measured with error, a common challenge in many epidemiological studies. Traditional deconvolution estimators rely only on assumptions about the distribution of X and the error in its measurement, and ignore information available in auxiliary variables. Our method assumes the availability of a covariate vector statistically related to X by a mean-variance function regression model, where regression errors are normally distributed and independent of the measurement errors. Simulations suggest that the estimator achieves a much lower integrated squared error than the observed-data kernel density estimator when models are correctly specified and the assumption of normal regression errors is met. We illustrate the method using anthropometric measurements of newborns to estimate the density function of newborn length. Copyright © 2011 John Wiley & Sons, Ltd.

Analysis of measured data of human body based on error correcting frequency

NASA Astrophysics Data System (ADS)

Jin, Aiyan; Peipei, Gao; Shang, Xiaomei

2014-04-01

Anthropometry is to measure all parts of human body surface, and the measured data is the basis of analysis and study of the human body, establishment and modification of garment size and formulation and implementation of online clothing store. In this paper, several groups of the measured data are gained, and analysis of data error is gotten by analyzing the error frequency and using analysis of variance method in mathematical statistics method. Determination of the measured data accuracy and the difficulty of measured parts of human body, further studies of the causes of data errors, and summarization of the key points to minimize errors possibly are also mentioned in the paper. This paper analyses the measured data based on error frequency, and in a way , it provides certain reference elements to promote the garment industry development.

Modeling the subfilter scalar variance for large eddy simulation in forced isotropic turbulence

NASA Astrophysics Data System (ADS)

Cheminet, Adam; Blanquart, Guillaume

2011-11-01

Static and dynamic model for the subfilter scalar variance in homogeneous isotropic turbulence are investigated using direct numerical simulations (DNS) of a lineary forced passive scalar field. First, we introduce a new scalar forcing technique conditioned only on the scalar field which allows the fluctuating scalar field to reach a statistically stationary state. Statistical properties, including 2nd and 3rd statistical moments, spectra, and probability density functions of the scalar field have been analyzed. Using this technique, we performed constant density and variable density DNS of scalar mixing in isotropic turbulence. The results are used in an a-priori study of scalar variance models. Emphasis is placed on further studying the dynamic model introduced by G. Balarac, H. Pitsch and V. Raman [Phys. Fluids 20, (2008)]. Scalar variance models based on Bedford and Yeo's expansion are accurate for small filter width but errors arise in the inertial subrange. Results suggest that a constant coefficient computed from an assumed Kolmogorov spectrum is often sufficient to predict the subfilter scalar variance.

Assessment of the relative merits of a few methods to detect evolutionary trends.

PubMed

Laurin, Michel

2010-12-01

Some of the most basic questions about the history of life concern evolutionary trends. These include determining whether or not metazoans have become more complex over time, whether or not body size tends to increase over time (the Cope-Depéret rule), or whether or not brain size has increased over time in various taxa, such as mammals and birds. Despite the proliferation of studies on such topics, assessment of the reliability of results in this field is hampered by the variability of techniques used and the lack of statistical validation of these methods. To solve this problem, simulations are performed using a variety of evolutionary models (gradual Brownian motion, speciational Brownian motion, and Ornstein-Uhlenbeck), with or without a drift of variable amplitude, with variable variance of tips, and with bounds placed close or far from the starting values and final means of simulated characters. These are used to assess the relative merits (power, Type I error rate, bias, and mean absolute value of error on slope estimate) of several statistical methods that have recently been used to assess the presence of evolutionary trends in comparative data. Results show widely divergent performance of the methods. The simple, nonphylogenetic regression (SR) and variance partitioning using phylogenetic eigenvector regression (PVR) with a broken stick selection procedure have greatly inflated Type I error rate (0.123-0.180 at a 0.05 threshold), which invalidates their use in this context. However, they have the greatest power. Most variants of Felsenstein's independent contrasts (FIC; five of which are presented) have adequate Type I error rate, although two have a slightly inflated Type I error rate with at least one of the two reference trees (0.064-0.090 error rate at a 0.05 threshold). The power of all contrast-based methods is always much lower than that of SR and PVR, except under Brownian motion with a strong trend and distant bounds. Mean absolute value of error on slope of all FIC methods is slightly higher than that of phylogenetic generalized least squares (PGLS), SR, and PVR. PGLS performs well, with low Type I error rate, low error on regression coefficient, and power comparable with some FIC methods. Four variants of skewness analysis are examined, and a new method to assess significance of results is presented. However, all have consistently low power, except in rare combinations of trees, trend strength, and distance between final means and bounds. Globally, the results clearly show that FIC-based methods and PGLS are globally better than nonphylogenetic methods and variance partitioning with PVR. FIC methods and PGLS are sensitive to the model of evolution (and, hence, to branch length errors). Our results suggest that regressing raw character contrasts against raw geological age contrasts yields a good combination of power and Type I error rate. New software to facilitate batch analysis is presented.

Spatio-temporal error growth in the multi-scale Lorenz'96 model

NASA Astrophysics Data System (ADS)

Herrera, S.; Fernández, J.; Rodríguez, M. A.; Gutiérrez, J. M.

2010-07-01

The influence of multiple spatio-temporal scales on the error growth and predictability of atmospheric flows is analyzed throughout the paper. To this aim, we consider the two-scale Lorenz'96 model and study the interplay of the slow and fast variables on the error growth dynamics. It is shown that when the coupling between slow and fast variables is weak the slow variables dominate the evolution of fluctuations whereas in the case of strong coupling the fast variables impose a non-trivial complex error growth pattern on the slow variables with two different regimes, before and after saturation of fast variables. This complex behavior is analyzed using the recently introduced Mean-Variance Logarithmic (MVL) diagram.

Large Area Crop Inventory Experiment (LACIE). Phase 1: Evaluation report

NASA Technical Reports Server (NTRS)

1976-01-01

It appears that the Large Area Crop Inventory Experiment over the Great Plains, can with a reasonable expectation, be a satisfactory component of a 90/90 production estimator. The area estimator produced more accurate area estimates for the total winter wheat region than for the mixed spring and winter wheat region of the northern Great Plains. The accuracy does appear to degrade somewhat in regions of marginal agriculture where there are small fields and abundant confusion crops. However, it would appear that these regions tend also to be marginal with respect to wheat production and thus increased area estimation errors do not greatly influence the overall production estimation accuracy in the United States. The loss of segments resulting from cloud cover appears to be a random phenomenon that introduces no significant bias into the estimates. This loss does increase the variance of the estimates.

Simple analytical model for low-frequency frequency-modulation noise of monolithic tunable lasers.

PubMed

Huynh, Tam N; Ó Dúill, Seán P; Nguyen, Lim; Rusch, Leslie A; Barry, Liam P

2014-02-10

We employ simple analytical models to construct the entire frequency-modulation (FM)-noise spectrum of tunable semiconductor lasers. Many contributions to the laser FM noise can be clearly identified from the FM-noise spectrum, such as standard Weiner FM noise incorporating laser relaxation oscillation, excess FM noise due to thermal fluctuations, and carrier-induced refractive index fluctuations from stochastic carrier generation in the passive tuning sections. The contribution of the latter effect is identified by noting a correlation between part of the FM-noise spectrum with the FM-modulation response of the passive sections. We pay particular attention to the case of widely tunable lasers with three independent tuning sections, mainly the sampled-grating distributed Bragg reflector laser, and compare with that of a distributed feedback laser. The theoretical model is confirmed with experimental measurements, with the calculations of the important phase-error variance demonstrating excellent agreement.

Meta-analysis with missing study-level sample variance data.

PubMed

Chowdhry, Amit K; Dworkin, Robert H; McDermott, Michael P

2016-07-30

We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing-completely-at-random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression to impute the missing sample variances. Our method takes advantage of study-level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross-over studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Experimental investigation of correlation between fading and glint for aircraft targets

NASA Astrophysics Data System (ADS)

Wallin, C. M.; Aas, B.

The correlation between the fading and glint of aircraft targets is investigated experimentally using a conventional amplitude comparison three-channel monopulse radar operating in the Ku-band. A significant correlation is found between the RCS and the variance of the angle error signals; this correlation seems to be independent of the aspect angle. The correlation between the RCS and the angle error signals themselves, however, is found to be very small.

Using Fisher Information Criteria for Chemical Sensor Selection via Convex Optimization Methods

DTIC Science & Technology

2016-11-16

determinant of the inverse Fisher information matrix which is proportional to the global error volume. If a practitioner has a suitable...pro- ceeds from the determinant of the inverse Fisher information matrix which is proportional to the global error volume. If a practitioner has a...design of statistical estimators (i.e. sensors) as their respective inverses act as lower bounds to the (co)variances of the subject estimator, a property

Improved Horvitz-Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology

PubMed Central

Breslow, Norman E.; Lumley, Thomas; Ballantyne, Christie M; Chambless, Lloyd E.; Kulich, Michal

2009-01-01

The case-cohort study involves two-phase sampling: simple random sampling from an infinite super-population at phase one and stratified random sampling from a finite cohort at phase two. Standard analyses of case-cohort data involve solution of inverse probability weighted (IPW) estimating equations, with weights determined by the known phase two sampling fractions. The variance of parameter estimates in (semi)parametric models, including the Cox model, is the sum of two terms: (i) the model based variance of the usual estimates that would be calculated if full data were available for the entire cohort; and (ii) the design based variance from IPW estimation of the unknown cohort total of the efficient influence function (IF) contributions. This second variance component may be reduced by adjusting the sampling weights, either by calibration to known cohort totals of auxiliary variables correlated with the IF contributions or by their estimation using these same auxiliary variables. Both adjustment methods are implemented in the R survey package. We derive the limit laws of coefficients estimated using adjusted weights. The asymptotic results suggest practical methods for construction of auxiliary variables that are evaluated by simulation of case-cohort samples from the National Wilms Tumor Study and by log-linear modeling of case-cohort data from the Atherosclerosis Risk in Communities Study. Although not semiparametric efficient, estimators based on adjusted weights may come close to achieving full efficiency within the class of augmented IPW estimators. PMID:20174455

Analysis of the Precision of Variable Flip Angle T1 Mapping with Emphasis on the Noise Propagated from RF Transmit Field Maps.

PubMed

Lee, Yoojin; Callaghan, Martina F; Nagy, Zoltan

2017-01-01

In magnetic resonance imaging, precise measurements of longitudinal relaxation time ( T 1 ) is crucial to acquire useful information that is applicable to numerous clinical and neuroscience applications. In this work, we investigated the precision of T 1 relaxation time as measured using the variable flip angle method with emphasis on the noise propagated from radiofrequency transmit field ([Formula: see text]) measurements. The analytical solution for T 1 precision was derived by standard error propagation methods incorporating the noise from the three input sources: two spoiled gradient echo (SPGR) images and a [Formula: see text] map. Repeated in vivo experiments were performed to estimate the total variance in T 1 maps and we compared these experimentally obtained values with the theoretical predictions to validate the established theoretical framework. Both the analytical and experimental results showed that variance in the [Formula: see text] map propagated comparable noise levels into the T 1 maps as either of the two SPGR images. Improving precision of the [Formula: see text] measurements significantly reduced the variance in the estimated T 1 map. The variance estimated from the repeatedly measured in vivo T 1 maps agreed well with the theoretically-calculated variance in T 1 estimates, thus validating the analytical framework for realistic in vivo experiments. We concluded that for T 1 mapping experiments, the error propagated from the [Formula: see text] map must be considered. Optimizing the SPGR signals while neglecting to improve the precision of the [Formula: see text] map may result in grossly overestimating the precision of the estimated T 1 values.

A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality

NASA Astrophysics Data System (ADS)

Cheung, KW; So, HC; Ma, W.-K.; Chan, YT

2006-12-01

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time-of-arrival (TOA), received signal strength (RSS), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the mobile station. In this paper, we present a constrained weighted least squares (CWLS) mobile positioning approach that encompasses all the above described measurement cases. The advantages of CWLS include performance optimality and capability of extension to hybrid measurement cases (e.g., mobile positioning using TDOA and AOA measurements jointly). Assuming zero-mean uncorrelated measurement errors, we show by mean and variance analysis that all the developed CWLS location estimators achieve zero bias and the Cramér-Rao lower bound approximately when measurement error variances are small. The asymptotic optimum performance is also confirmed by simulation results.

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

The causal link among militarization, economic growth, CO2 emission, and energy consumption.

PubMed

Bildirici, Melike E

2017-02-01

This paper examines the long-run and the causal relationship among CO 2 emissions, militarization, economic growth, and energy consumption for USA for the period 1960-2013. Using the bound test approach to cointegration, a short-run as well as a long-run relationship among the variables with a positive and a statistically significant relationship between CO 2 emissions and militarization was found. To determine the causal link, MWALD and Rao's F tests were applied. According to Rao's F tests, the evidence of a unidirectional causality running from militarization to CO 2 emissions, from energy consumption to CO 2 emissions, and from militarization to energy consumption all without a feedback was found. Further, the results determined that 26% of the forecast-error variance of CO 2 emissions was explained by the forecast error variance of militarization and 60% by energy consumption.

Hedging Your Bets by Learning Reward Correlations in the Human Brain

PubMed Central

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

Absolute calibration of optical flats

DOEpatents

Sommargren, Gary E.

2005-04-05

The invention uses the phase shifting diffraction interferometer (PSDI) to provide a true point-by-point measurement of absolute flatness over the surface of optical flats. Beams exiting the fiber optics in a PSDI have perfect spherical wavefronts. The measurement beam is reflected from the optical flat and passed through an auxiliary optic to then be combined with the reference beam on a CCD. The combined beams include phase errors due to both the optic under test and the auxiliary optic. Standard phase extraction algorithms are used to calculate this combined phase error. The optical flat is then removed from the system and the measurement fiber is moved to recombine the two beams. The newly combined beams include only the phase errors due to the auxiliary optic. When the second phase measurement is subtracted from the first phase measurement, the absolute phase error of the optical flat is obtained.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan

PubMed Central

2017-01-01

Background Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Materials and Methods Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician’s request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. Results The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results. PMID:28107395

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan.

PubMed

Najat, Dereen

2017-01-01

Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician's request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results.

Estimation variance bounds of importance sampling simulations in digital communication systems

NASA Technical Reports Server (NTRS)

Lu, D.; Yao, K.

1991-01-01

In practical applications of importance sampling (IS) simulation, two basic problems are encountered, that of determining the estimation variance and that of evaluating the proper IS parameters needed in the simulations. The authors derive new upper and lower bounds on the estimation variance which are applicable to IS techniques. The upper bound is simple to evaluate and may be minimized by the proper selection of the IS parameter. Thus, lower and upper bounds on the improvement ratio of various IS techniques relative to the direct Monte Carlo simulation are also available. These bounds are shown to be useful and computationally simple to obtain. Based on the proposed technique, one can readily find practical suboptimum IS parameters. Numerical results indicate that these bounding techniques are useful for IS simulations of linear and nonlinear communication systems with intersymbol interference in which bit error rate and IS estimation variances cannot be obtained readily using prior techniques.

A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?

PubMed Central

Mumford, Jeanette A.

2017-01-01

Even after thorough preprocessing and a careful time series analysis of functional magnetic resonance imaging (fMRI) data, artifact and other issues can lead to violations of the assumption that the variance is constant across subjects in the group level model. This is especially concerning when modeling a continuous covariate at the group level, as the slope is easily biased by outliers. Various models have been proposed to deal with outliers including models that use the first level variance or that use the group level residual magnitude to differentially weight subjects. The most typically used robust regression, implementing a robust estimator of the regression slope, has been previously studied in the context of fMRI studies and was found to perform well in some scenarios, but a loss of Type I error control can occur for some outlier settings. A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500–1000 subjects). The Type I error control with smaller sample sizes has not been studied in this model and has not been compared to other modeling approaches that handle outliers such as FSL’s Flame 1 and FSL’s outlier de-weighting. Focusing on group level inference with a continuous covariate over a range of sample sizes and degree of heteroscedasticity, which can be driven either by the within- or between-subject variability, both styles of robust regression are compared to ordinary least squares (OLS), FSL’s Flame 1, Flame 1 with outlier de-weighting algorithm and Kendall’s Tau. Additionally, subject omission using the Cook’s Distance measure with OLS and nonparametric inference with the OLS statistic are studied. Pros and cons of these models as well as general strategies for detecting outliers in data and taking precaution to avoid inflated Type I error rates are discussed. PMID:28030782

Theta EEG dynamics of the error-related negativity.

PubMed

Trujillo, Logan T; Allen, John J B

2007-03-01

The error-related negativity (ERN) is a response-locked brain potential (ERP) occurring 80-100ms following response errors. This report contrasts three views of the genesis of the ERN, testing the classic view that time-locked phasic bursts give rise to the ERN against the view that the ERN arises from a pure phase-resetting of ongoing theta (4-7Hz) EEG activity and the view that the ERN is generated - at least in part - by a phase-resetting and amplitude enhancement of ongoing theta EEG activity. Time-domain ERP analyses were augmented with time-frequency investigations of phase-locked and non-phase-locked spectral power, and inter-trial phase coherence (ITPC) computed from individual EEG trials, examining time courses and scalp topographies. Simulations based on the assumptions of the classic, pure phase-resetting, and phase-resetting plus enhancement views, using parameters from each subject's empirical data, were used to contrast the time-frequency findings that could be expected if one or more of these hypotheses adequately modeled the data. Error responses produced larger amplitude activity than correct responses in time-domain ERPs immediately following responses, as expected. Time-frequency analyses revealed that significant error-related post-response increases in total spectral power (phase- and non-phase-locked), phase-locked power, and ITPC were primarily restricted to the theta range, with this effect located over midfrontocentral sites, with a temporal distribution from approximately 150-200ms prior to the button press and persisting up to 400ms post-button press. The increase in non-phase-locked power (total power minus phase-locked power) was larger than phase-locked power, indicating that the bulk of the theta event-related dynamics were not phase-locked to response. Results of the simulations revealed a good fit for data simulated according to the phase-locking with amplitude enhancement perspective, and a poor fit for data simulated according to the classic view and the pure phase-resetting view. Error responses produce not only phase-locked increases in theta EEG activity, but also increases in non-phase-locked theta, both of which share a similar topography. The findings are thus consistent with the notion advanced by Luu et al. [Luu P, Tucker DM, Makeig S. Frontal midline theta and the error-related negativity; neurophysiological mechanisms of action regulation. Clin Neurophysiol 2004;115:1821-35] that the ERN emerges, at least in part, from a phase-resetting and phase-locking of ongoing theta-band activity, in the context of a general increase in theta power following errors.

Error modeling for differential GPS. M.S. Thesis - MIT, 12 May 1995

NASA Technical Reports Server (NTRS)

Blerman, Gregory S.

1995-01-01

Differential Global Positioning System (DGPS) positioning is used to accurately locate a GPS receiver based upon the well-known position of a reference site. In utilizing this technique, several error sources contribute to position inaccuracy. This thesis investigates the error in DGPS operation and attempts to develop a statistical model for the behavior of this error. The model for DGPS error is developed using GPS data collected by Draper Laboratory. The Marquardt method for nonlinear curve-fitting is used to find the parameters of a first order Markov process that models the average errors from the collected data. The results show that a first order Markov process can be used to model the DGPS error as a function of baseline distance and time delay. The model's time correlation constant is 3847.1 seconds (1.07 hours) for the mean square error. The distance correlation constant is 122.8 kilometers. The total process variance for the DGPS model is 3.73 sq meters.

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

PubMed

Fischer, A; Friggens, N C; Berry, D P; Faverdin, P

2018-07-01

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Anatomy of emotion: a 3D study of facial mimicry.

PubMed

Ferrario, V F; Sforza, C

2007-01-01

Alterations in facial motion severely impair the quality of life and social interaction of patients, and an objective grading of facial function is necessary. A method for the non-invasive detection of 3D facial movements was developed. Sequences of six standardized facial movements (maximum smile; free smile; surprise with closed mouth; surprise with open mouth; right side eye closure; left side eye closure) were recorded in 20 healthy young adults (10 men, 10 women) using an optoelectronic motion analyzer. For each subject, 21 cutaneous landmarks were identified by 2-mm reflective markers, and their 3D movements during each facial animation were computed. Three repetitions of each expression were recorded (within-session error), and four separate sessions were used (between-session error). To assess the within-session error, the technical error of the measurement (random error, TEM) was computed separately for each sex, movement and landmark. To assess the between-session repeatability, the standard deviation among the mean displacements of each landmark (four independent sessions) was computed for each movement. TEM for the single landmarks ranged between 0.3 and 9.42 mm (intrasession error). The sex- and movement-related differences were statistically significant (two-way analysis of variance, p=0.003 for sex comparison, p=0.009 for the six movements, p<0.001 for the sex x movement interaction). Among four different (independent) sessions, the left eye closure had the worst repeatability, the right eye closure had the best one; the differences among various movements were statistically significant (one-way analysis of variance, p=0.041). In conclusion, the current protocol demonstrated a sufficient repeatability for a future clinical application. Great care should be taken to assure a consistent marker positioning in all the subjects.

Bias Correction and Random Error Characterization for the Assimilation of HRDI Line-of-Sight Wind Measurements

NASA Technical Reports Server (NTRS)

Tangborn, Andrew; Menard, Richard; Ortland, David; Einaudi, Franco (Technical Monitor)

2001-01-01

A new approach to the analysis of systematic and random observation errors is presented in which the error statistics are obtained using forecast data rather than observations from a different instrument type. The analysis is carried out at an intermediate retrieval level, instead of the more typical state variable space. This method is carried out on measurements made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). HRDI, a limb sounder, is the only satellite instrument measuring winds in the stratosphere, and the only instrument of any kind making global wind measurements in the upper atmosphere. HRDI measures doppler shifts in the two different O2 absorption bands (alpha and B) and the retrieved products are tangent point Line-of-Sight wind component (level 2 retrieval) and UV winds (level 3 retrieval). This analysis is carried out on a level 1.9 retrieval, in which the contributions from different points along the line-of-sight have not been removed. Biases are calculated from O-F (observed minus forecast) LOS wind components and are separated into a measurement parameter space consisting of 16 different values. The bias dependence on these parameters (plus an altitude dependence) is used to create a bias correction scheme carried out on the level 1.9 retrieval. The random error component is analyzed by separating the gamma and B band observations and locating observation pairs where both bands are very nearly looking at the same location at the same time. It is shown that the two observation streams are uncorrelated and that this allows the forecast error variance to be estimated. The bias correction is found to cut the effective observation error variance in half.

Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

NASA Astrophysics Data System (ADS)

de Montera, L.; Mallet, C.; Barthès, L.; Golé, P.

2008-08-01

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20 50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction.

PubMed

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-02-27

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10 16 electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

PubMed Central

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-01-01

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. PMID:28264424

Global Gravity Wave Variances from Aura MLS: Characteristics and Interpretation

DTIC Science & Technology

2008-12-01

slight longitudinal variations, with secondary high- latitude peaks occurring over Greenland and Europe . As the QBO changes to the westerly phase, the...equatorial GW temperature variances from suborbital data (e.g., Eck- ermann et al. 1995). The extratropical wave variances are generally larger in the...emanating from tropopause altitudes, presumably radiated from tropospheric jet stream in- stabilities associated with baroclinic storm systems that

Adaptive Optics for Turbulent Shear Layers

DTIC Science & Technology

2006-12-20

which is related to the phase variance through the wave number). Once obtained 5 by whatever means, the phase variance was used to compute an...new inlet for a wider test section and included a downstream throat to chock the flow and prevent pressure disturbances from the vacuum pumps from...wavefronts in Figure 15 are for an aperture size that captures only about half a cycle; the streamnwise length of the half cycle being -- 10 cm ( 4 in

An investigation of error correcting techniques for OMV and AXAF

NASA Technical Reports Server (NTRS)

Ingels, Frank; Fryer, John

1991-01-01

The original objectives of this project were to build a test system for the NASA 255/223 Reed/Solomon encoding/decoding chip set and circuit board. This test system was then to be interfaced with a convolutional system at MSFC to examine the performance of the concantinated codes. After considerable work, it was discovered that the convolutional system could not function as needed. This report documents the design, construction, and testing of the test apparatus for the R/S chip set. The approach taken was to verify the error correcting behavior of the chip set by injecting known error patterns onto data and observing the results. Error sequences were generated using pseudo-random number generator programs, with Poisson time distribution between errors and Gaussian burst lengths. Sample means, variances, and number of un-correctable errors were calculated for each data set before testing.

M-071 critical data analysis

NASA Technical Reports Server (NTRS)

Hegsted, D. M.

1975-01-01

A prototype balance study was conducted on earth prior to the balance studies conducted in Skylab itself. Collected were daily dietary intake data of 6 minerals and nitrogen, and fecal and urinary outputs on each of three astronauts. Essential statistical issues show what quantities need to be estimated and establish the scope of inference associated with alternative variance estimates. The procedures for obtaining the final variability due both to errors of measurement and total error (total = measurement and biological variability) are exhibited.

Reliable absolute analog code retrieval approach for 3D measurement

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Chen, Deyun

2017-11-01

The wrapped phase of phase-shifting approach can be unwrapped by using Gray code, but both the wrapped phase error and Gray code decoding error can result in period jump error, which will lead to gross measurement error. Therefore, this paper presents a reliable absolute analog code retrieval approach. The combination of unequal-period Gray code and phase shifting patterns at high frequencies are used to obtain high-frequency absolute analog code, and at low frequencies, the same unequal-period combination patterns are used to obtain the low-frequency absolute analog code. Next, the difference between the two absolute analog codes was employed to eliminate period jump errors, and a reliable unwrapped result can be obtained. Error analysis was used to determine the applicable conditions, and this approach was verified through theoretical analysis. The proposed approach was further verified experimentally. Theoretical analysis and experimental results demonstrate that the proposed approach can perform reliable analog code unwrapping.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

PubMed

Spiess, Martin; Jordan, Pascal; Wendt, Mike

2018-05-07

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Implementation of a flow-dependent background error correlation length scale formulation in the NEMOVAR OSTIA system

NASA Astrophysics Data System (ADS)

Fiedler, Emma; Mao, Chongyuan; Good, Simon; Waters, Jennifer; Martin, Matthew

2017-04-01

OSTIA is the Met Office's Operational Sea Surface Temperature (SST) and Ice Analysis system, which produces L4 (globally complete, gridded) analyses on a daily basis. Work is currently being undertaken to replace the original OI (Optimal Interpolation) data assimilation scheme with NEMOVAR, a 3D-Var data assimilation method developed for use with the NEMO ocean model. A dual background error correlation length scale formulation is used for SST in OSTIA, as implemented in NEMOVAR. Short and long length scales are combined according to the ratio of the decomposition of the background error variances into short and long spatial correlations. The pre-defined background error variances vary spatially and seasonally, but not on shorter time-scales. If the derived length scales applied to the daily analysis are too long, SST features may be smoothed out. Therefore a flow-dependent component to determining the effective length scale has also been developed. The total horizontal gradient of the background SST field is used to identify regions where the length scale should be shortened. These methods together have led to an improvement in the resolution of SST features compared to the previous OI analysis system, without the introduction of spurious noise. This presentation will show validation results for feature resolution in OSTIA using the OI scheme, the dual length scale NEMOVAR scheme, and the flow-dependent implementation.

Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.

Distributions in the error space: goal-directed movements described in time and state-space representations.

PubMed

Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L

2014-01-01

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Between-Batch Pharmacokinetic Variability Inflates Type I Error Rate in Conventional Bioequivalence Trials: A Randomized Advair Diskus Clinical Trial.

PubMed

Burmeister Getz, E; Carroll, K J; Mielke, J; Benet, L Z; Jones, B

2017-03-01

We previously demonstrated pharmacokinetic differences among manufacturing batches of a US Food and Drug Administration (FDA)-approved dry powder inhalation product (Advair Diskus 100/50) large enough to establish between-batch bio-inequivalence. Here, we provide independent confirmation of pharmacokinetic bio-inequivalence among Advair Diskus 100/50 batches, and quantify residual and between-batch variance component magnitudes. These variance estimates are used to consider the type I error rate of the FDA's current two-way crossover design recommendation. When between-batch pharmacokinetic variability is substantial, the conventional two-way crossover design cannot accomplish the objectives of FDA's statistical bioequivalence test (i.e., cannot accurately estimate the test/reference ratio and associated confidence interval). The two-way crossover, which ignores between-batch pharmacokinetic variability, yields an artificially narrow confidence interval on the product comparison. The unavoidable consequence is type I error rate inflation, to ∼25%, when between-batch pharmacokinetic variability is nonzero. This risk of a false bioequivalence conclusion is substantially higher than asserted by regulators as acceptable consumer risk (5%). © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

[Locally weighted least squares estimation of DPOAE evoked by continuously sweeping primaries].

PubMed

Han, Xiaoli; Fu, Xinxing; Cui, Jie; Xiao, Ling

2013-12-01

Distortion product otoacoustic emission (DPOAE) signal can be used for diagnosis of hearing loss so that it has an important clinical value. Continuously using sweeping primaries to measure DPOAE provides an efficient tool to record DPOAE data rapidly when DPOAE is measured in a large frequency range. In this paper, locally weighted least squares estimation (LWLSE) of 2f1-f2 DPOAE is presented based on least-squares-fit (LSF) algorithm, in which DPOAE is evoked by continuously sweeping tones. In our study, we used a weighted error function as the loss function and the weighting matrixes in the local sense to obtain a smaller estimated variance. Firstly, ordinary least squares estimation of the DPOAE parameters was obtained. Then the error vectors were grouped and the different local weighting matrixes were calculated in each group. And finally, the parameters of the DPOAE signal were estimated based on least squares estimation principle using the local weighting matrixes. The simulation results showed that the estimate variance and fluctuation errors were reduced, so the method estimates DPOAE and stimuli more accurately and stably, which facilitates extraction of clearer DPOAE fine structure.

Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

NASA Astrophysics Data System (ADS)

Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

2018-02-01

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

Impact of electronic chemotherapy order forms on prescribing errors at an urban medical center: results from an interrupted time-series analysis.

PubMed

Elsaid, K; Truong, T; Monckeberg, M; McCarthy, H; Butera, J; Collins, C

2013-12-01

To evaluate the impact of electronic standardized chemotherapy templates on incidence and types of prescribing errors. A quasi-experimental interrupted time series with segmented regression. A 700-bed multidisciplinary tertiary care hospital with an ambulatory cancer center. A multidisciplinary team including oncology physicians, nurses, pharmacists and information technologists. Standardized, regimen-specific, chemotherapy prescribing forms were developed and implemented over a 32-month period. Trend of monthly prevented prescribing errors per 1000 chemotherapy doses during the pre-implementation phase (30 months), immediate change in the error rate from pre-implementation to implementation and trend of errors during the implementation phase. Errors were analyzed according to their types: errors in communication or transcription, errors in dosing calculation and errors in regimen frequency or treatment duration. Relative risk (RR) of errors in the post-implementation phase (28 months) compared with the pre-implementation phase was computed with 95% confidence interval (CI). Baseline monthly error rate was stable with 16.7 prevented errors per 1000 chemotherapy doses. A 30% reduction in prescribing errors was observed with initiating the intervention. With implementation, a negative change in the slope of prescribing errors was observed (coefficient = -0.338; 95% CI: -0.612 to -0.064). The estimated RR of transcription errors was 0.74; 95% CI (0.59-0.92). The estimated RR of dosing calculation errors was 0.06; 95% CI (0.03-0.10). The estimated RR of chemotherapy frequency/duration errors was 0.51; 95% CI (0.42-0.62). Implementing standardized chemotherapy-prescribing templates significantly reduced all types of prescribing errors and improved chemotherapy safety.

Measuring kinetics of complex single ion channel data using mean-variance histograms.

PubMed

Patlak, J B

1993-07-01

The measurement of single ion channel kinetics is difficult when those channels exhibit subconductance events. When the kinetics are fast, and when the current magnitudes are small, as is the case for Na+, Ca2+, and some K+ channels, these difficulties can lead to serious errors in the estimation of channel kinetics. I present here a method, based on the construction and analysis of mean-variance histograms, that can overcome these problems. A mean-variance histogram is constructed by calculating the mean current and the current variance within a brief "window" (a set of N consecutive data samples) superimposed on the digitized raw channel data. Systematic movement of this window over the data produces large numbers of mean-variance pairs which can be assembled into a two-dimensional histogram. Defined current levels (open, closed, or sublevel) appear in such plots as low variance regions. The total number of events in such low variance regions is estimated by curve fitting and plotted as a function of window width. This function decreases with the same time constants as the original dwell time probability distribution for each of the regions. The method can therefore be used: 1) to present a qualitative summary of the single channel data from which the signal-to-noise ratio, open channel noise, steadiness of the baseline, and number of conductance levels can be quickly determined; 2) to quantify the dwell time distribution in each of the levels exhibited. In this paper I present the analysis of a Na+ channel recording that had a number of complexities. The signal-to-noise ratio was only about 8 for the main open state, open channel noise, and fast flickers to other states were present, as were a substantial number of subconductance states. "Standard" half-amplitude threshold analysis of these data produce open and closed time histograms that were well fitted by the sum of two exponentials, but with apparently erroneous time constants, whereas the mean-variance histogram technique provided a more credible analysis of the open, closed, and subconductance times for the patch. I also show that the method produces accurate results on simulated data in a wide variety of conditions, whereas the half-amplitude method, when applied to complex simulated data shows the same errors as were apparent in the real data. The utility and the limitations of this new method are discussed.

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, R. M.; Tai, K.-S.

2013-01-01

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

A MIMO radar quadrature and multi-channel amplitude-phase error combined correction method based on cross-correlation

NASA Astrophysics Data System (ADS)

Yun, Lingtong; Zhao, Hongzhong; Du, Mengyuan

2018-04-01

Quadrature and multi-channel amplitude-phase error have to be compensated in the I/Q quadrature sampling and signal through multi-channel. A new method that it doesn't need filter and standard signal is presented in this paper. And it can combined estimate quadrature and multi-channel amplitude-phase error. The method uses cross-correlation and amplitude ratio between the signal to estimate the two amplitude-phase errors simply and effectively. And the advantages of this method are verified by computer simulation. Finally, the superiority of the method is also verified by measure data of outfield experiments.

Analytic aerotriangulation utilizing Skylab earth terrain camera (S-190B) photography. [from Charlotte, North Carolina to Rappahannock River in Virginia

NASA Technical Reports Server (NTRS)

Keller, M. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Inherent errors in using nonmetric Skylab photography and office-identified photo control made it necessary to perform numerous block adjustment solutions involving different combinations of control and weights. The final block adjustment was executed holding to 14 of the office-identified photo control points. Solution accuracy was evaluated by comparing the analytically computed ground positions of the withheld photo control points with their known ground positions and also by determining the standard errors of these points from variance values. A horizontal position RMS error of 15 meters was attained. The maximum observed error in position at a control point was 25 meters.

Empirical performance of interpolation techniques in risk-neutral density (RND) estimation

NASA Astrophysics Data System (ADS)

Bahaludin, H.; Abdullah, M. H.

2017-03-01

The objective of this study is to evaluate the empirical performance of interpolation techniques in risk-neutral density (RND) estimation. Firstly, the empirical performance is evaluated by using statistical analysis based on the implied mean and the implied variance of RND. Secondly, the interpolation performance is measured based on pricing error. We propose using the leave-one-out cross-validation (LOOCV) pricing error for interpolation selection purposes. The statistical analyses indicate that there are statistical differences between the interpolation techniques:second-order polynomial, fourth-order polynomial and smoothing spline. The results of LOOCV pricing error shows that interpolation by using fourth-order polynomial provides the best fitting to option prices in which it has the lowest value error.

Optimized mirror shape tuning using beam weightings based on distance, angle of incidence, reflectivity, and power.

PubMed

Goldberg, Kenneth A; Yashchuk, Valeriy V

2016-05-01

For glancing-incidence optical systems, such as short-wavelength optics used for nano-focusing, incorporating physical factors in the calculations used for shape optimization can improve performance. Wavefront metrology, including the measurement of a mirror's shape or slope, is routinely used as input for mirror figure optimization on mirrors that can be bent, actuated, positioned, or aligned. Modeling shows that when the incident power distribution, distance from focus, angle of incidence, and the spatially varying reflectivity are included in the optimization, higher Strehl ratios can be achieved. Following the works of Maréchal and Mahajan, optimization of the Strehl ratio (for peak intensity with a coherently illuminated system) occurs when the expectation value of the phase error's variance is minimized. We describe an optimization procedure based on regression analysis that incorporates these physical parameters. This approach is suitable for coherently illuminated systems of nearly diffraction-limited quality. Mathematically, this work is an enhancement of the methods commonly applied for ex situ alignment based on uniform weighting of all points on the surface (or a sub-region of the surface). It follows a similar approach to the optimization of apodized and non-uniformly illuminated optical systems. Significantly, it reaches a different conclusion than a more recent approach based on minimization of focal plane ray errors.

The relation of hand and arm configuration variances while tracking geometric figures in Parkinson's disease: aspects for rehabilitation.

PubMed

Keresztényi, Zoltán; Cesari, Paola; Fazekas, Gábor; Laczkó, József

2009-03-01

Variances of drawing arm movements between patients with Parkinson's disease and healthy controls were compared. The aim was to determine whether differences in joint synergies or individual joint rotations affect the endpoint (hand position) variance. Joint and endpoint coordinates were measured while participants performed drawing tasks. Variances of arm configurations and endpoints were computed and statistically analyzed for 12 patients and 12 controls. The variance of arm movements for patients (both for arm configuration and endpoint) was overall higher than that for the control group. Variation was smaller for drawing a circle versus a square and for drawing with the dominant versus the nondominant hand within both groups. The ratio of arm configuration variances between groups was similar to the ratio of endpoint variances. There were significant differences in the velocity, but not in the path lengths of movements comparing the two groups. Patients presented less movement stability while drawing different figures in different trials. Moreover, the similarity of the ratios suggests that the ill-coordinated hand movement was caused by the error in the movements of individual body parts rather than by the lack of intersegmental coordination. Thus, rehabilitation may focus on the improvement of the precision of individual joint rotations.

Detection of gene–environment interaction in pedigree data using genome-wide genotypes

PubMed Central

Nivard, Michel G; Middeldorp, Christel M; Lubke, Gitta; Hottenga, Jouke-Jan; Abdellaoui, Abdel; Boomsma, Dorret I; Dolan, Conor V

2016-01-01

Heritability may be estimated using phenotypic data collected in relatives or in distantly related individuals using genome-wide single nucleotide polymorphism (SNP) data. We combined these approaches by re-parameterizing the model proposed by Zaitlen et al and extended this model to include moderation of (total and SNP-based) genetic and environmental variance components by a measured moderator. By means of data simulation, we demonstrated that the type 1 error rates of the proposed test are correct and parameter estimates are accurate. As an application, we considered the moderation by age or year of birth of variance components associated with body mass index (BMI), height, attention problems (AP), and symptoms of anxiety and depression. The genetic variance of BMI was found to increase with age, but the environmental variance displayed a greater increase with age, resulting in a proportional decrease of the heritability of BMI. Environmental variance of height increased with year of birth. The environmental variance of AP increased with age. These results illustrate the assessment of moderation of environmental and genetic effects, when estimating heritability from combined SNP and family data. The assessment of moderation of genetic and environmental variance will enhance our understanding of the genetic architecture of complex traits. PMID:27436263

Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies.

PubMed

Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C

2013-03-01

Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.

Estimating pixel variances in the scenes of staring sensors

DOEpatents

Simonson, Katherine M [Cedar Crest, NM; Ma, Tian J [Albuquerque, NM

2012-01-24

A technique for detecting changes in a scene perceived by a staring sensor is disclosed. The technique includes acquiring a reference image frame and a current image frame of a scene with the staring sensor. A raw difference frame is generated based upon differences between the reference image frame and the current image frame. Pixel error estimates are generated for each pixel in the raw difference frame based at least in part upon spatial error estimates related to spatial intensity gradients in the scene. The pixel error estimates are used to mitigate effects of camera jitter in the scene between the current image frame and the reference image frame.

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

Repeatable source, site, and path effects on the standard deviation for empirical ground-motion prediction models

USGS Publications Warehouse

Lin, P.-S.; Chiou, B.; Abrahamson, N.; Walling, M.; Lee, C.-T.; Cheng, C.-T.

2011-01-01

In this study, we quantify the reduction in the standard deviation for empirical ground-motion prediction models by removing ergodic assumption.We partition the modeling error (residual) into five components, three of which represent the repeatable source-location-specific, site-specific, and path-specific deviations from the population mean. A variance estimation procedure of these error components is developed for use with a set of recordings from earthquakes not heavily clustered in space.With most source locations and propagation paths sampled only once, we opt to exploit the spatial correlation of residuals to estimate the variances associated with the path-specific and the source-location-specific deviations. The estimation procedure is applied to ground-motion amplitudes from 64 shallow earthquakes in Taiwan recorded at 285 sites with at least 10 recordings per site. The estimated variance components are used to quantify the reduction in aleatory variability that can be used in hazard analysis for a single site and for a single path. For peak ground acceleration and spectral accelerations at periods of 0.1, 0.3, 0.5, 1.0, and 3.0 s, we find that the singlesite standard deviations are 9%-14% smaller than the total standard deviation, whereas the single-path standard deviations are 39%-47% smaller.

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

USGS Publications Warehouse

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Testing physical models for dipolar asymmetry with CMB polarization

NASA Astrophysics Data System (ADS)

Contreras, D.; Zibin, J. P.; Scott, D.; Banday, A. J.; Górski, K. M.

2017-12-01

The cosmic microwave background (CMB) temperature anisotropies exhibit a large-scale dipolar power asymmetry. To determine whether this is due to a real, physical modulation or is simply a large statistical fluctuation requires the measurement of new modes. Here we forecast how well CMB polarization data from Planck and future experiments will be able to confirm or constrain physical models for modulation. Fitting several such models to the Planck temperature data allows us to provide predictions for polarization asymmetry. While for some models and parameters Planck polarization will decrease error bars on the modulation amplitude by only a small percentage, we show, importantly, that cosmic-variance-limited (and in some cases even Planck) polarization data can decrease the errors by considerably better than the expectation of √{2 } based on simple ℓ-space arguments. We project that if the primordial fluctuations are truly modulated (with parameters as indicated by Planck temperature data) then Planck will be able to make a 2 σ detection of the modulation model with 20%-75% probability, increasing to 45%-99% when cosmic-variance-limited polarization is considered. We stress that these results are quite model dependent. Cosmic variance in temperature is important: combining statistically isotropic polarization with temperature data will spuriously increase the significance of the temperature signal with 30% probability for Planck.

Neuroanatomical dissociation for taxonomic and thematic knowledge in the human brain

PubMed Central

Schwartz, Myrna F.; Kimberg, Daniel Y.; Walker, Grant M.; Brecher, Adelyn; Faseyitan, Olufunsho K.; Dell, Gary S.; Mirman, Daniel; Coslett, H. Branch

2011-01-01

It is thought that semantic memory represents taxonomic information differently from thematic information. This study investigated the neural basis for the taxonomic-thematic distinction in a unique way. We gathered picture-naming errors from 86 individuals with poststroke language impairment (aphasia). Error rates were determined separately for taxonomic errors (“pear” in response to apple) and thematic errors (“worm” in response to apple), and their shared variance was regressed out of each measure. With the segmented lesions normalized to a common template, we carried out voxel-based lesion-symptom mapping on each error type separately. We found that taxonomic errors localized to the left anterior temporal lobe and thematic errors localized to the left temporoparietal junction. This is an indication that the contribution of these regions to semantic memory cleaves along taxonomic-thematic lines. Our findings show that a distinction long recognized in the psychological sciences is grounded in the structure and function of the human brain. PMID:21540329

Analysis of overdispersed count data: application to the Human Papillomavirus Infection in Men (HIM) Study.

PubMed

Lee, J-H; Han, G; Fulp, W J; Giuliano, A R

2012-06-01

The Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading conclusion. We illustrated the use of four models for overdispersed count data that may be attributed to excessive zeros. These are Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial models. The example data in this article deal with the number of incidents involving human papillomavirus infection. The four models resulted in differing statistical inferences. The Poisson model, which is widely used in epidemiology research, underestimated the standard errors and overstated the significance of some covariates.

General Monte Carlo reliability simulation code including common mode failures and HARP fault/error-handling

NASA Technical Reports Server (NTRS)

Platt, M. E.; Lewis, E. E.; Boehm, F.

1991-01-01

A Monte Carlo Fortran computer program was developed that uses two variance reduction techniques for computing system reliability applicable to solving very large highly reliable fault-tolerant systems. The program is consistent with the hybrid automated reliability predictor (HARP) code which employs behavioral decomposition and complex fault-error handling models. This new capability is called MC-HARP which efficiently solves reliability models with non-constant failures rates (Weibull). Common mode failure modeling is also a specialty.

SU-E-T-192: FMEA Severity Scores - Do We Really Know?

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

Tonigan, J; Johnson, J; Kry, S

2014-06-01

Purpose: Failure modes and effects analysis (FMEA) is a subjective risk mitigation technique that has not been applied to physics-specific quality management practices. There is a need for quantitative FMEA data as called for in the literature. This work focuses specifically on quantifying FMEA severity scores for physics components of IMRT delivery and comparing to subjective scores. Methods: Eleven physical failure modes (FMs) for head and neck IMRT dose calculation and delivery are examined near commonly accepted tolerance criteria levels. Phantom treatment planning studies and dosimetry measurements (requiring decommissioning in several cases) are performed to determine the magnitude of dosemore » delivery errors for the FMs (i.e., severity of the FM). Resultant quantitative severity scores are compared to FMEA scores obtained through an international survey and focus group studies. Results: Physical measurements for six FMs have resulted in significant PTV dose errors up to 4.3% as well as close to 1 mm significant distance-to-agreement error between PTV and OAR. Of the 129 survey responses, the vast majority of the responders used Varian machines with Pinnacle and Eclipse planning systems. The average years of experience was 17, yet familiarity with FMEA less than expected. Survey reports perception of dose delivery error magnitude varies widely, in some cases 50% difference in dose delivery error expected amongst respondents. Substantial variance is also seen for all FMs in occurrence, detectability, and severity scores assigned with average variance values of 5.5, 4.6, and 2.2, respectively. Survey shows for MLC positional FM(2mm) average of 7.6% dose error expected (range 0–50%) compared to 2% error seen in measurement. Analysis of ranking in survey, treatment planning studies, and quantitative value comparison will be presented. Conclusion: Resultant quantitative severity scores will expand the utility of FMEA for radiotherapy and verify accuracy of FMEA results compared to highly variable subjective scores.« less

Image Reconstruction for Interferometric Imaging of Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

DeSantis, Zachary J.

Imaging distant objects at a high resolution has always presented a challenge due to the diffraction limit. Larger apertures improve the resolution, but at some point the cost of engineering, building, and correcting phase aberrations of large apertures become prohibitive. Interferometric imaging uses the Van Cittert-Zernike theorem to form an image from measurements of spatial coherence. This effectively allows the synthesis of a large aperture from two or more smaller telescopes to improve the resolution. We apply this method to imaging geosynchronous satellites with a ground-based system. Imaging a dim object from the ground presents unique challenges. The atmosphere creates errors in the phase measurements. The measurements are taken simultaneously across a large bandwidth of light. The atmospheric piston error, therefore, manifests as a linear phase error across the spectral measurements. Because the objects are faint, many of the measurements are expected to have a poor signal-to-noise ratio (SNR). This eliminates possibility of use of commonly used techniques like closure phase, which is a standard technique in astronomical interferometric imaging for making partial phase measurements in the presence of atmospheric error. The bulk of our work has been focused on forming an image, using sub-Nyquist sampled data, in the presence of these linear phase errors without relying on closure phase techniques. We present an image reconstruction algorithm that successfully forms an image in the presence of these linear phase errors. We demonstrate our algorithm’s success in both simulation and in laboratory experiments.

Imaging shear wave propagation for elastic measurement using OCT Doppler variance method

NASA Astrophysics Data System (ADS)

Zhu, Jiang; Miao, Yusi; Qu, Yueqiao; Ma, Teng; Li, Rui; Du, Yongzhao; Huang, Shenghai; Shung, K. Kirk; Zhou, Qifa; Chen, Zhongping

2016-03-01

In this study, we have developed an acoustic radiation force orthogonal excitation optical coherence elastography (ARFOE-OCE) method for the visualization of the shear wave and the calculation of the shear modulus based on the OCT Doppler variance method. The vibration perpendicular to the OCT detection direction is induced by the remote acoustic radiation force (ARF) and the shear wave propagating along the OCT beam is visualized by the OCT M-scan. The homogeneous agar phantom and two-layer agar phantom are measured using the ARFOE-OCE system. The results show that the ARFOE-OCE system has the ability to measure the shear modulus beyond the OCT imaging depth. The OCT Doppler variance method, instead of the OCT Doppler phase method, is used for vibration detection without the need of high phase stability and phase wrapping correction. An M-scan instead of the B-scan for the visualization of the shear wave also simplifies the data processing.

Criterion Predictability: Identifying Differences Between [r-squares

ERIC Educational Resources Information Center

Malgady, Robert G.

1976-01-01

An analysis of variance procedure for testing differences in r-squared, the coefficient of determination, across independent samples is proposed and briefly discussed. The principal advantage of the procedure is to minimize Type I error for follow-up tests of pairwise differences. (Author/JKS)

Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

PubMed

Etzel, C J; Shete, S; Beasley, T M; Fernandez, J R; Allison, D B; Amos, C I

2003-01-01

Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests using the non-transformed, skewed phenotypes, from 7.5 to 20.1% after Winsorizing and from 12.6 to 33.2% after Box-Cox transformation. Likewise, power (adjusted for empirical type 1 error) using leptokurtic phenotypes at the 0.01 alpha level ranged from 4.4 to 12.5% across all tests with no transformation, from 7 to 19.2% after Winsorizing and from 4.5 to 13.8% after Box-Cox transformation. Thus the Box-Cox transformation apparently provided the best type 1 error control and maximal power among the procedures we considered for analyzing a non-normal, skewed distribution (chi2) while Winzorizing worked best for the non-normal, kurtic distribution (Laplace). We repeated the same simulations using a larger sample size (200 sib pairs) and found similar results. Copyright 2003 S. Karger AG, Basel

Propagation of uncertainty by Monte Carlo simulations in case of basic geodetic computations

NASA Astrophysics Data System (ADS)

Wyszkowska, Patrycja

2017-12-01

The determination of the accuracy of functions of measured or adjusted values may be a problem in geodetic computations. The general law of covariance propagation or in case of the uncorrelated observations the propagation of variance (or the Gaussian formula) are commonly used for that purpose. That approach is theoretically justified for the linear functions. In case of the non-linear functions, the first-order Taylor series expansion is usually used but that solution is affected by the expansion error. The aim of the study is to determine the applicability of the general variance propagation law in case of the non-linear functions used in basic geodetic computations. The paper presents errors which are a result of negligence of the higher-order expressions and it determines the range of such simplification. The basis of that analysis is the comparison of the results obtained by the law of propagation of variance and the probabilistic approach, namely Monte Carlo simulations. Both methods are used to determine the accuracy of the following geodetic computations: the Cartesian coordinates of unknown point in the three-point resection problem, azimuths and distances of the Cartesian coordinates, height differences in the trigonometric and the geometric levelling. These simulations and the analysis of the results confirm the possibility of applying the general law of variance propagation in basic geodetic computations even if the functions are non-linear. The only condition is the accuracy of observations, which cannot be too low. Generally, this is not a problem with using present geodetic instruments.

A hybrid method for synthetic aperture ladar phase-error compensation

NASA Astrophysics Data System (ADS)

Hua, Zhili; Li, Hongping; Gu, Yongjian

2009-07-01

As a high resolution imaging sensor, synthetic aperture ladar data contain phase-error whose source include uncompensated platform motion and atmospheric turbulence distortion errors. Two previously devised methods, rank one phase-error estimation algorithm and iterative blind deconvolution are reexamined, of which a hybrid method that can recover both the images and PSF's without any a priori information on the PSF is built to speed up the convergence rate by the consideration in the choice of initialization. To be integrated into spotlight mode SAL imaging model respectively, three methods all can effectively reduce the phase-error distortion. For each approach, signal to noise ratio, root mean square error and CPU time are computed, from which we can see the convergence rate of the hybrid method can be improved because a more efficient initialization set of blind deconvolution. Moreover, by making a further discussion of the hybrid method, the weight distribution of ROPE and IBD is found to be an important factor that affects the final result of the whole compensation process.

[Medication reconciliation: an innovative experience in an internal medicine unit to decrease errors due to inacurrate medication histories].

PubMed

Pérennes, Maud; Carde, Axel; Nicolas, Xavier; Dolz, Manuel; Bihannic, René; Grimont, Pauline; Chapot, Thierry; Granier, Hervé

2012-03-01

An inaccurate medication history may prevent the discovery of a pre-admission iatrogenic event or lead to interrupted drug therapy during hospitalization. Medication reconciliation is a process that ensures the transfer of medication information at admission to the hospital. The aims of this prospective study were to evaluate the interest in clinical practice of this concept and the resources needed for its implementation. We chose to include patients aged 65 years or over admitted in the internal medicine unit between June and October 2010. We obtained an accurate list of each patient's home medications. This list was then compared with medication orders. All medication variances were classified as intended or unintended. An internist and a pharmacist classified the clinical importance of each unintended variance. Sixty-one patients (mean age: 78 ± 7.4 years) were included in our study. We identified 38 unintended discrepancies. The average number of unintended discrepancies was 0.62 per patient. Twenty-five patients (41%) had one or more unintended discrepancies at admission. The contact with the community pharmacist permitted us to identify 21 (55%) unintended discrepancies. The most common errors were the omission of a regularly used medication (76%) and an incorrect dosage (16%). Our intervention resulted in order changes by the physician for 30 (79%) unintended discrepancies. Fifty percent of the unintended variances were judged by the internist and 76% by the pharmacist to be clinically significant. The admission to the hospital is a critical transition point for the continuity of care in medication management. Medication reconciliation can identify and resolve errors due to inaccurate medication histories. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

A Dynamic Attitude Measurement System Based on LINS

PubMed Central

Li, Hanzhou; Pan, Quan; Wang, Xiaoxu; Zhang, Juanni; Li, Jiang; Jiang, Xiangjun

2014-01-01

A dynamic attitude measurement system (DAMS) is developed based on a laser inertial navigation system (LINS). Three factors of the dynamic attitude measurement error using LINS are analyzed: dynamic error, time synchronization and phase lag. An optimal coning errors compensation algorithm is used to reduce coning errors, and two-axis wobbling verification experiments are presented in the paper. The tests indicate that the attitude accuracy is improved 2-fold by the algorithm. In order to decrease coning errors further, the attitude updating frequency is improved from 200 Hz to 2000 Hz. At the same time, a novel finite impulse response (FIR) filter with three notches is designed to filter the dither frequency of the ring laser gyro (RLG). The comparison tests suggest that the new filter is five times more effective than the old one. The paper indicates that phase-frequency characteristics of FIR filter and first-order holder of navigation computer constitute the main sources of phase lag in LINS. A formula to calculate the LINS attitude phase lag is introduced in the paper. The expressions of dynamic attitude errors induced by phase lag are derived. The paper proposes a novel synchronization mechanism that is able to simultaneously solve the problems of dynamic test synchronization and phase compensation. A single-axis turntable and a laser interferometer are applied to verify the synchronization mechanism. The experiments results show that the theoretically calculated values of phase lag and attitude error induced by phase lag can both match perfectly with testing data. The block diagram of DAMS and physical photos are presented in the paper. The final experiments demonstrate that the real-time attitude measurement accuracy of DAMS can reach up to 20″ (1σ) and the synchronization error is less than 0.2 ms on the condition of three axes wobbling for 10 min. PMID:25177802

Sensitivity analysis of simulated SOA loadings using a variance-based statistical approach: SENSITIVITY ANALYSIS OF SOA

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

Shrivastava, Manish; Zhao, Chun; Easter, Richard C.

We investigate the sensitivity of secondary organic aerosol (SOA) loadings simulated by a regional chemical transport model to 7 selected tunable model parameters: 4 involving emissions of anthropogenic and biogenic volatile organic compounds, anthropogenic semi-volatile and intermediate volatility organics (SIVOCs), and NOx, 2 involving dry deposition of SOA precursor gases, and one involving particle-phase transformation of SOA to low volatility. We adopt a quasi-Monte Carlo sampling approach to effectively sample the high-dimensional parameter space, and perform a 250 member ensemble of simulations using a regional model, accounting for some of the latest advances in SOA treatments based on our recentmore » work. We then conduct a variance-based sensitivity analysis using the generalized linear model method to study the responses of simulated SOA loadings to the tunable parameters. Analysis of SOA variance from all 250 simulations shows that the volatility transformation parameter, which controls whether particle-phase transformation of SOA from semi-volatile SOA to non-volatile is on or off, is the dominant contributor to variance of simulated surface-level daytime SOA (65% domain average contribution). We also split the simulations into 2 subsets of 125 each, depending on whether the volatility transformation is turned on/off. For each subset, the SOA variances are dominated by the parameters involving biogenic VOC and anthropogenic SIVOC emissions. Furthermore, biogenic VOC emissions have a larger contribution to SOA variance when the SOA transformation to non-volatile is on, while anthropogenic SIVOC emissions have a larger contribution when the transformation is off. NOx contributes less than 4.3% to SOA variance, and this low contribution is mainly attributed to dominance of intermediate to high NOx conditions throughout the simulated domain. The two parameters related to dry deposition of SOA precursor gases also have very low contributions to SOA variance. This study highlights the large sensitivity of SOA loadings to the particle-phase transformation of SOA volatility, which is neglected in most previous models.« less

Continued investigation of potential application of Omega navigation to civil aviation

NASA Technical Reports Server (NTRS)

Baxa, E. G., Jr.

1978-01-01

Major attention is given to an analysis of receiver repeatability in measuring OMEGA phase data. Repeatability is defined as the ability of two like receivers which are co-located to achieve the same LOP phase readings. Specific data analysis is presented. A propagation model is described which has been used in the analysis of propagation anomalies. Composite OMEGA analysis is presented in terms of carrier phase correlation analysis and the determination of carrier phase weighting coefficients for minimizing composite phase variation. Differential OMEGA error analysis is presented for receiver separations. Three frequency analysis includes LOP error and position error based on three and four OMEGA transmissions. Results of phase amplitude correlation studies are presented.

Optimized tuner selection for engine performance estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

2013-01-01

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Imaging phased telescope array study

NASA Technical Reports Server (NTRS)

Harvey, James E.

1989-01-01

The problems encountered in obtaining a wide field-of-view with large, space-based direct imaging phased telescope arrays were considered. After defining some of the critical systems issues, previous relevant work in the literature was reviewed and summarized. An extensive list was made of potential error sources and the error sources were categorized in the form of an error budget tree including optical design errors, optical fabrication errors, assembly and alignment errors, and environmental errors. After choosing a top level image quality requirment as a goal, a preliminary tops-down error budget allocation was performed; then, based upon engineering experience, detailed analysis, or data from the literature, a bottoms-up error budget reallocation was performed in an attempt to achieve an equitable distribution of difficulty in satisfying the various allocations. This exercise provided a realistic allocation for residual off-axis optical design errors in the presence of state-of-the-art optical fabrication and alignment errors. Three different computational techniques were developed for computing the image degradation of phased telescope arrays due to aberrations of the individual telescopes. Parametric studies and sensitivity analyses were then performed for a variety of subaperture configurations and telescope design parameters in an attempt to determine how the off-axis performance of a phased telescope array varies as the telescopes are scaled up in size. The Air Force Weapons Laboratory (AFWL) multipurpose telescope testbed (MMTT) configuration was analyzed in detail with regard to image degradation due to field curvature and distortion of the individual telescopes as they are scaled up in size.

Simulator Study of Helmet-Mounted Symbology System Concepts in Degraded Visual Environments.

PubMed

Cheung, Bob; McKinley, Richard A; Steels, Brad; Sceviour, Robert; Cosman, Vaughn; Holst, Peter

2015-07-01

A sudden loss of external visual cues during critical phases of flight results in spatial disorientation. This is due to undetected horizontal and vertical drift when there is little tolerance for error and correction delay as the helicopter is close to the ground. Three helmet-mounted symbology system concepts were investigated in the simulator as potential solutions for the legacy Griffon helicopters. Thirteen Royal Canadian Air Force (RCAF) Griffon pilots were exposed to the Helmet Display Tracking System for Degraded Visual Environments (HDTS), the BrownOut Symbology System (BOSS), and the current RCAF AVS7 symbology system. For each symbology system, the pilot performed a two-stage departure and a single-stage approach. The presentation order of the symbology systems was randomized. Objective performance metrics included aircraft speed, altitude, attitude, and distance from the landing point. Subjective measurements included situation awareness, mental effort, perceived performance, perceptual cue rating, and NASA Task Load Index. Repeated measures analysis of variance and subsequent planned comparison for all the objective and subjective measurements were performed between the AVS7, HDTS, and BOSS. Our results demonstrated that HDTS and BOSS showed general improvement over AVS7 in two-stage departure. However, only HDTS performed significantly better in heading error than AVS7. During the single-stage approach, BOSS performed worse than AVS7 in heading root mean square error, and only HDTS performed significantly better in distance to landing point and approach heading than the others. Both the HDTS and BOSS possess their own limitations; however, HDTS is the pilots' preferred flight display.

Refractive index variance of cells and tissues measured by quantitative phase imaging.

PubMed

Shan, Mingguang; Kandel, Mikhail E; Popescu, Gabriel

2017-01-23

The refractive index distribution of cells and tissues governs their interaction with light and can report on morphological modifications associated with disease. Through intensity-based measurements, refractive index information can be extracted only via scattering models that approximate light propagation. As a result, current knowledge of refractive index distributions across various tissues and cell types remains limited. Here we use quantitative phase imaging and the statistical dispersion relation (SDR) to extract information about the refractive index variance in a variety of specimens. Due to the phase-resolved measurement in three-dimensions, our approach yields refractive index results without prior knowledge about the tissue thickness. With the recent progress in quantitative phase imaging systems, we anticipate that using SDR will become routine in assessing tissue optical properties.

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

NASA Astrophysics Data System (ADS)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with distinct statistical structures.

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.

Errors in the estimation of approximate entropy and other recurrence-plot-derived indices due to the finite resolution of RR time series.

PubMed

García-González, Miguel A; Fernández-Chimeno, Mireya; Ramos-Castro, Juan

2009-02-01

An analysis of the errors due to the finite resolution of RR time series in the estimation of the approximate entropy (ApEn) is described. The quantification errors in the discrete RR time series produce considerable errors in the ApEn estimation (bias and variance) when the signal variability or the sampling frequency is low. Similar errors can be found in indices related to the quantification of recurrence plots. An easy way to calculate a figure of merit [the signal to resolution of the neighborhood ratio (SRN)] is proposed in order to predict when the bias in the indices could be high. When SRN is close to an integer value n, the bias is higher than when near n - 1/2 or n + 1/2. Moreover, if SRN is close to an integer value, the lower this value, the greater the bias is.

Quantitative evaluation of statistical errors in small-angle X-ray scattering measurements

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

Sedlak, Steffen M.; Bruetzel, Linda K.; Lipfert, Jan

A new model is proposed for the measurement errors incurred in typical small-angle X-ray scattering (SAXS) experiments, which takes into account the setup geometry and physics of the measurement process. The model accurately captures the experimentally determined errors from a large range of synchrotron and in-house anode-based measurements. Its most general formulation gives for the variance of the buffer-subtracted SAXS intensity σ 2(q) = [I(q) + const.]/(kq), whereI(q) is the scattering intensity as a function of the momentum transferq;kand const. are fitting parameters that are characteristic of the experimental setup. The model gives a concrete procedure for calculating realistic measurementmore » errors for simulated SAXS profiles. In addition, the results provide guidelines for optimizing SAXS measurements, which are in line with established procedures for SAXS experiments, and enable a quantitative evaluation of measurement errors.« less

Carrier recovery methods for a dual-mode modem: A design approach

NASA Technical Reports Server (NTRS)

Richards, C. W.; Wilson, S. G.

1984-01-01

A dual mode model with selectable QPSK or 16-QASK modulation schemes is discussed. The theoretical reasoning as well as the practical trade-offs made during the development of a modem are presented, with attention given to the carrier recovery method used for coherent demodulation. Particular attention is given to carrier recovery methods that can provide little degradation due to phase error for both QPSK and 16-QASK, while being insensitive to the amplitude characteristic of a 16-QASK modulation scheme. A computer analysis of the degradation is symbol error rate (SER) for QPSK and 16-QASK due to phase error is prresented. Results find that an energy increase of roughly 4 dB is needed to maintain a SER of 1X10(-5) for QPSK with 20 deg of phase error and 16-QASK with 7 deg phase error.

Incorporating measurement error in n = 1 psychological autoregressive modeling.

PubMed

Schuurman, Noémi K; Houtveen, Jan H; Hamaker, Ellen L

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

Film thickness measurement based on nonlinear phase analysis using a Linnik microscopic white-light spectral interferometer.

PubMed

Guo, Tong; Chen, Zhuo; Li, Minghui; Wu, Juhong; Fu, Xing; Hu, Xiaotang

2018-04-20

Based on white-light spectral interferometry and the Linnik microscopic interference configuration, the nonlinear phase components of the spectral interferometric signal were analyzed for film thickness measurement. The spectral interferometric signal was obtained using a Linnik microscopic white-light spectral interferometer, which includes the nonlinear phase components associated with the effective thickness, the nonlinear phase error caused by the double-objective lens, and the nonlinear phase of the thin film itself. To determine the influence of the effective thickness, a wavelength-correction method was proposed that converts the effective thickness into a constant value; the nonlinear phase caused by the effective thickness can then be determined and subtracted from the total nonlinear phase. A method for the extraction of the nonlinear phase error caused by the double-objective lens was also proposed. Accurate thickness measurement of a thin film can be achieved by fitting the nonlinear phase of the thin film after removal of the nonlinear phase caused by the effective thickness and by the nonlinear phase error caused by the double-objective lens. The experimental results demonstrated that both the wavelength-correction method and the extraction method for the nonlinear phase error caused by the double-objective lens improve the accuracy of film thickness measurements.

Quantifying phase synchronization using instances of Hilbert phase slips

NASA Astrophysics Data System (ADS)

Govindan, R. B.

2018-07-01

We propose to quantify phase synchronization between two signals, x(t) and y(t), by calculating variance in the Hilbert phase of y(t) at instances of phase slips exhibited by x(t). The proposed approach is tested on numerically simulated coupled chaotic Roessler systems and second order autoregressive processes. Furthermore we compare the performance of the proposed and original approaches using uterine electromyogram signals and show that both approaches yield consistent results A standard phase synchronization approach, which involves unwrapping the Hilbert phases (ϕ1(t) and ϕ2(t)) of the two signals and analyzing the variance in the | n ṡϕ1(t) - m ṡϕ2(t) | , mod 2 π, (n and m are integers), was used for comparison. The synchronization indexes obtained from the proposed approach and the standard approach agree reasonably well in all of the systems studied in this work. Our results indicate that the proposed approach, unlike the traditional approach, does not require the non-invertible transformations - unwrapping of the phases and calculation of mod 2 π and it can be used to reliably to quantify phase synchrony between two signals.

Gamma model and its analysis for phase measuring profilometry.

PubMed

Liu, Kai; Wang, Yongchang; Lau, Daniel L; Hao, Qi; Hassebrook, Laurence G

2010-03-01

Phase measuring profilometry is a method of structured light illumination whose three-dimensional reconstructions are susceptible to error from nonunitary gamma in the associated optical devices. While the effects of this distortion diminish with an increasing number of employed phase-shifted patterns, gamma distortion may be unavoidable in real-time systems where the number of projected patterns is limited by the presence of target motion. A mathematical model is developed for predicting the effects of nonunitary gamma on phase measuring profilometry, while also introducing an accurate gamma calibration method and two strategies for minimizing gamma's effect on phase determination. These phase correction strategies include phase corrections with and without gamma calibration. With the reduction in noise, for three-step phase measuring profilometry, analysis of the root mean squared error of the corrected phase will show a 60x reduction in phase error when the proposed gamma calibration is performed versus 33x reduction without calibration.

The Age of At Variance.

ERIC Educational Resources Information Center

DeMott, Benjamin

1990-01-01

A faculty member at Amherst discusses the challenges that have shaken his "self-edifice." He says there was strain "in the scrambling, adjusting, re-doing, remodeling of the mind, and in the constant collisions with past fatuity and obliviousness." Mina Shaughnessy's "Errors and Expectations" is recommended. (MLW)

Real-Time Detector of Human Fatigue: Detecting Lapses in Alertness

DTIC Science & Technology

2008-02-15

These coefficients and their variances, covariances and standard errors were computed simultaneously using HLM 6 (Raudenbush, Bryk, Cheong, & Congdon ...CA: Sage. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon , R. T. (2004). HLM6: Hierarchical Linear and Nonlinear Modeling [Computer software

Trajectory of frequency stability in typical development.

PubMed

Frohlich, Joel; Irimia, Andrei; Jeste, Shafali S

2015-03-01

This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = -0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.

Measurement equivalence of seven selected items of posttraumatic growth between black and white adult survivors of Hurricane Katrina.

PubMed

Rhodes, Alison M; Tran, Thanh V

2013-02-01

This study examined the equivalence or comparability of the measurement properties of seven selected items measuring posttraumatic growth among self-identified Black (n = 270) and White (n = 707) adult survivors of Hurricane Katrina, using data from the Baseline Survey of the Hurricane Katrina Community Advisory Group Study. Internal consistency reliability was equally good for both groups (Cronbach's alphas = .79), as were correlations between individual scale items and their respective overall scale. Confirmatory factor analysis of a congeneric measurement model of seven selected items of posttraumatic growth showed adequate measures of fit for both groups. The results showed only small variation in magnitude of factor loadings and measurement errors between the two samples. Tests of measurement invariance showed mixed results, but overall indicated that factor loading, error variance, and factor variance were similar between the two samples. These seven selected items can be useful for future large-scale surveys of posttraumatic growth.

Non-Gaussian Distribution of DNA Barcode Extension In Nanochannels Using High-throughput Imaging

NASA Astrophysics Data System (ADS)

Sheats, Julian; Reinhart, Wesley; Reifenberger, Jeff; Gupta, Damini; Muralidhar, Abhiram; Cao, Han; Dorfman, Kevin

2015-03-01

We present experimental data for the extension of internal segments of highly confined DNA using a high-­throughput experimental setup. Barcode­-labeled E. coli genomic DNA molecules were imaged at a high areal density in square nanochannels with sizes ranging from 40 nm to 51 nm in width. Over 25,000 molecules were used to obtain more than 1,000,000 measurements for genomic distances between 2,500 bp and 100,000 bp. The distribution of extensions has positive excess kurtosis and is skew­ left due to weak backfolding in the channel. As a result, the two Odijk theories for the chain extension and variance bracket the experimental data. We compared to predictions of a harmonic approximation for the confinement free energy and show that it produces a substantial error in the variance. These results suggest an inherent error associated with any statistical analysis of barcoded DNA that relies on harmonic models for chain extension. Present address: Department of Chemical and Biological Engineering, Princeton University.

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

Quantization error of CCD cameras and their influence on phase calculation in fringe pattern analysis.

PubMed

Skydan, Oleksandr A; Lilley, Francis; Lalor, Michael J; Burton, David R

2003-09-10

We present an investigation into the phase errors that occur in fringe pattern analysis that are caused by quantization effects. When acquisition devices with a limited value of camera bit depth are used, there are a limited number of quantization levels available to record the signal. This may adversely affect the recorded signal and adds a potential source of instrumental error to the measurement system. Quantization effects also determine the accuracy that may be achieved by acquisition devices in a measurement system. We used the Fourier fringe analysis measurement technique. However, the principles can be applied equally well for other phase measuring techniques to yield a phase error distribution that is caused by the camera bit depth.

Phase-demodulation error of a fiber-optic Fabry-Perot sensor with complex reflection coefficients.

PubMed

Kilpatrick, J M; MacPherson, W N; Barton, J S; Jones, J D

2000-03-20

The influence of reflector losses attracts little discussion in standard treatments of the Fabry-Perot interferometer yet may be an important factor contributing to errors in phase-stepped demodulation of fiber optic Fabry-Perot (FFP) sensors. We describe a general transfer function for FFP sensors with complex reflection coefficients and estimate systematic phase errors that arise when the asymmetry of the reflected fringe system is neglected, as is common in the literature. The measured asymmetric response of higher-finesse metal-dielectric FFP constructions corroborates a model that predicts systematic phase errors of 0.06 rad in three-step demodulation of a low-finesse FFP sensor (R = 0.05) with internal reflector losses of 25%.

[Fire behavior of Mongolian oak leaves fuel bed under no-wind and zero-slope conditions. II. Analysis of the factors affecting flame length and residence time and related prediction models].

PubMed

Zhang, Ji-Li; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Jin, Sen

2012-11-01

Taking fuel moisture content, fuel loading, and fuel bed depth as controlling factors, the fuel beds of Mongolian oak leaves in Maoershan region of Northeast China in field were simulated, and a total of one hundred experimental burnings under no-wind and zero-slope conditions were conducted in laboratory, with the effects of the fuel moisture content, fuel loading, and fuel bed depth on the flame length and its residence time analyzed and the multivariate linear prediction models constructed. The results indicated that fuel moisture content had a significant negative liner correlation with flame length, but less correlation with flame residence time. Both the fuel loading and the fuel bed depth were significantly positively correlated with flame length and its residence time. The interactions of fuel bed depth with fuel moisture content and fuel loading had significant effects on the flame length, while the interactions of fuel moisture content with fuel loading and fuel bed depth affected the flame residence time significantly. The prediction model of flame length had better prediction effect, which could explain 83.3% of variance, with a mean absolute error of 7.8 cm and a mean relative error of 16.2%, while the prediction model of flame residence time was not good enough, which could only explain 54% of variance, with a mean absolute error of 9.2 s and a mean relative error of 18.6%.

Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters.

PubMed

Chung, SungWon; Lu, Ying; Henry, Roland G

2006-11-01

Bootstrap is an empirical non-parametric statistical technique based on data resampling that has been used to quantify uncertainties of diffusion tensor MRI (DTI) parameters, useful in tractography and in assessing DTI methods. The current bootstrap method (repetition bootstrap) used for DTI analysis performs resampling within the data sharing common diffusion gradients, requiring multiple acquisitions for each diffusion gradient. Recently, wild bootstrap was proposed that can be applied without multiple acquisitions. In this paper, two new approaches are introduced called residual bootstrap and repetition bootknife. We show that repetition bootknife corrects for the large bias present in the repetition bootstrap method and, therefore, better estimates the standard errors. Like wild bootstrap, residual bootstrap is applicable to single acquisition scheme, and both are based on regression residuals (called model-based resampling). Residual bootstrap is based on the assumption that non-constant variance of measured diffusion-attenuated signals can be modeled, which is actually the assumption behind the widely used weighted least squares solution of diffusion tensor. The performances of these bootstrap approaches were compared in terms of bias, variance, and overall error of bootstrap-estimated standard error by Monte Carlo simulation. We demonstrate that residual bootstrap has smaller biases and overall errors, which enables estimation of uncertainties with higher accuracy. Understanding the properties of these bootstrap procedures will help us to choose the optimal approach for estimating uncertainties that can benefit hypothesis testing based on DTI parameters, probabilistic fiber tracking, and optimizing DTI methods.

Automated error correction in IBM quantum computer and explicit generalization

NASA Astrophysics Data System (ADS)

Ghosh, Debjit; Agarwal, Pratik; Pandey, Pratyush; Behera, Bikash K.; Panigrahi, Prasanta K.

2018-06-01

Construction of a fault-tolerant quantum computer remains a challenging problem due to unavoidable noise and fragile quantum states. However, this goal can be achieved by introducing quantum error-correcting codes. Here, we experimentally realize an automated error correction code and demonstrate the nondestructive discrimination of GHZ states in IBM 5-qubit quantum computer. After performing quantum state tomography, we obtain the experimental results with a high fidelity. Finally, we generalize the investigated code for maximally entangled n-qudit case, which could both detect and automatically correct any arbitrary phase-change error, or any phase-flip error, or any bit-flip error, or combined error of all types of error.

Observed spatiotemporal variability of boundary-layer turbulence over flat, heterogeneous terrain

NASA Astrophysics Data System (ADS)

Maurer, V.; Kalthoff, N.; Wieser, A.; Kohler, M.; Mauder, M.; Gantner, L.

2016-02-01

In the spring of 2013, extensive measurements with multiple Doppler lidar systems were performed. The instruments were arranged in a triangle with edge lengths of about 3 km in a moderately flat, agriculturally used terrain in northwestern Germany. For 6 mostly cloud-free convective days, vertical velocity variance profiles were calculated. Weighted-averaged surface fluxes proved to be more appropriate than data from individual sites for scaling the variance profiles; but even then, the scatter of profiles was mostly larger than the statistical error. The scatter could not be explained by mean wind speed or stability, whereas time periods with significantly increased variance contained broader thermals. Periods with an elevated maximum of the variance profiles could also be related to broad thermals. Moreover, statistically significant spatial differences of variance were found. They were not influenced by the existing surface heterogeneity. Instead, thermals were preserved between two sites when the travel time was shorter than the large-eddy turnover time. At the same time, no thermals passed for more than 2 h at a third site that was located perpendicular to the mean wind direction in relation to the first two sites. Organized structures of turbulence with subsidence prevailing in the surroundings of thermals can thus partly explain significant spatial variance differences existing for several hours. Therefore, the representativeness of individual variance profiles derived from measurements at a single site cannot be assumed.

Insar Unwrapping Error Correction Based on Quasi-Accurate Detection of Gross Errors (quad)

NASA Astrophysics Data System (ADS)

Kang, Y.; Zhao, C. Y.; Zhang, Q.; Yang, C. S.

2018-04-01

Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.

Sampling Variances and Covariances of Parameter Estimates in Item Response Theory.

DTIC Science & Technology

1982-08-01

substituting (15) into (16) and solving for k and K k = b b1 - o K , (17)k where b and b are means for m and r items, respectively. To find the variance...C5 , and C12 were treated as known. We find that the standard errors of B1 to B5 are increased drastically by ignorance of C 1 to C5 ; all...ERIC Facilltv-Acquisitlons Davie Hall 013A 4833 Rugby Avenue Chapel Hill, NC 27514 Bethesda, MD 20014 -7- Dr. A. J. Eschenbrenner 1 Dr. John R

Statistical aspects of the TNK-S2B trial of tenecteplase versus alteplase in acute ischemic stroke: an efficient, dose-adaptive, seamless phase II/III design.

PubMed

Levin, Bruce; Thompson, John L P; Chakraborty, Bibhas; Levy, Gilberto; MacArthur, Robert; Haley, E Clarke

2011-08-01

TNK-S2B, an innovative, randomized, seamless phase II/III trial of tenecteplase versus rt-PA for acute ischemic stroke, terminated for slow enrollment before regulatory approval of use of phase II patients in phase III. (1) To review the trial design and comprehensive type I error rate simulations and (2) to discuss issues raised during regulatory review, to facilitate future approval of similar designs. In phase II, an early (24-h) outcome and adaptive sequential procedure selected one of three tenecteplase doses for phase III comparison with rt-PA. Decision rules comparing this dose to rt-PA would cause stopping for futility at phase II end, or continuation to phase III. Phase III incorporated two co-primary hypotheses, allowing for a treatment effect at either end of the trichotomized Rankin scale. Assuming no early termination, four interim analyses and one final analysis of 1908 patients provided an experiment-wise type I error rate of <0.05. Over 1,000 distribution scenarios, each involving 40,000 replications, the maximum type I error in phase III was 0.038. Inflation from the dose selection was more than offset by the one-half continuity correction in the test statistics. Inflation from repeated interim analyses was more than offset by the reduction from the clinical stopping rules for futility at the first interim analysis. Design complexity and evolving regulatory requirements lengthened the review process. (1) The design was innovative and efficient. Per protocol, type I error was well controlled for the co-primary phase III hypothesis tests, and experiment-wise. (2a) Time must be allowed for communications with regulatory reviewers from first design stages. (2b) Adequate type I error control must be demonstrated. (2c) Greater clarity is needed on (i) whether this includes demonstration of type I error control if the protocol is violated and (ii) whether simulations of type I error control are acceptable. (2d) Regulatory agency concerns that protocols for futility stopping may not be followed may be allayed by submitting interim analysis results to them as these analyses occur.

A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output

PubMed Central

Stevanovic, Stefan; Pervan, Boris

2018-01-01

We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. PMID:29351250

Attitude and vibration control of a large flexible space-based antenna

NASA Technical Reports Server (NTRS)

Joshi, S. M.

1982-01-01

Control systems synthesis is considered for controlling the rigid body attitude and elastic motion of a large deployable space-based antenna. Two methods for control systems synthesis are considered. The first method utilizes the stability and robustness properties of the controller consisting of torque actuators and collocated attitude and rate sensors. The second method is based on the linear-quadratic-Gaussian control theory. A combination of the two methods, which results in a two level hierarchical control system, is also briefly discussed. The performance of the controllers is analyzed by computing the variances of pointing errors, feed misalignment errors and surface contour errors in the presence of sensor and actuator noise.

Attention failures versus misplaced diligence: separating attention lapses from speed-accuracy trade-offs.

PubMed

Seli, Paul; Cheyne, James Allan; Smilek, Daniel

2012-03-01

In two studies of a GO-NOGO task assessing sustained attention, we examined the effects of (1) altering speed-accuracy trade-offs through instructions (emphasizing both speed and accuracy or accuracy only) and (2) auditory alerts distributed throughout the task. Instructions emphasizing accuracy reduced errors and changed the distribution of GO trial RTs. Additionally, correlations between errors and increasing RTs produced a U-function; excessively fast and slow RTs accounted for much of the variance of errors. Contrary to previous reports, alerts increased errors and RT variability. The results suggest that (1) standard instructions for sustained attention tasks, emphasizing speed and accuracy equally, produce errors arising from attempts to conform to the misleading requirement for speed, which become conflated with attention-lapse produced errors and (2) auditory alerts have complex, and sometimes deleterious, effects on attention. We argue that instructions emphasizing accuracy provide a more precise assessment of attention lapses in sustained attention tasks. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Critical evaluation of connectivity-based point of care testing systems of glucose in a hospital environment.

PubMed

Floré, Katelijne M J; Fiers, Tom; Delanghe, Joris R

2008-01-01

In recent years a number of point of care testing (POCT) glucometers were introduced on the market. We investigated the analytical variability (lot-to-lot variation, calibration error, inter-instrument and inter-operator variability) of glucose POCT systems in a university hospital environment and compared these results with the analytical needs required for tight glucose monitoring. The reference hexokinase method was compared to different POCT systems based on glucose oxidase (blood gas instruments) or glucose dehydrogenase (handheld glucometers). Based upon daily internal quality control data, total errors were calculated for the various glucose methods and the analytical variability of the glucometers was estimated. The total error of the glucometers exceeded by far the desirable analytical specifications (based on a biological variability model). Lot-to-lot variation, inter-instrument variation and inter-operator variability contributed approximately equally to total variance. As in a hospital environment, distribution of hematocrit values is broad, converting blood glucose into plasma values using a fixed factor further increases variance. The percentage of outliers exceeded the ISO 15197 criteria in a broad glucose concentration range. Total analytical variation of handheld glucometers is larger than expected. Clinicians should be aware that the variability of glucose measurements obtained by blood gas instruments is lower than results obtained with handheld glucometers on capillary blood.

Measuring Cyclic Error in Laser Heterodyne Interferometers

NASA Technical Reports Server (NTRS)

Ryan, Daniel; Abramovici, Alexander; Zhao, Feng; Dekens, Frank; An, Xin; Azizi, Alireza; Chapsky, Jacob; Halverson, Peter

2010-01-01

An improved method and apparatus have been devised for measuring cyclic errors in the readouts of laser heterodyne interferometers that are configured and operated as displacement gauges. The cyclic errors arise as a consequence of mixing of spurious optical and electrical signals in beam launchers that are subsystems of such interferometers. The conventional approach to measurement of cyclic error involves phase measurements and yields values precise to within about 10 pm over air optical paths at laser wavelengths in the visible and near infrared. The present approach, which involves amplitude measurements instead of phase measurements, yields values precise to about .0.1 microns . about 100 times the precision of the conventional approach. In a displacement gauge of the type of interest here, the laser heterodyne interferometer is used to measure any change in distance along an optical axis between two corner-cube retroreflectors. One of the corner-cube retroreflectors is mounted on a piezoelectric transducer (see figure), which is used to introduce a low-frequency periodic displacement that can be measured by the gauges. The transducer is excited at a frequency of 9 Hz by a triangular waveform to generate a 9-Hz triangular-wave displacement having an amplitude of 25 microns. The displacement gives rise to both amplitude and phase modulation of the heterodyne signals in the gauges. The modulation includes cyclic error components, and the magnitude of the cyclic-error component of the phase modulation is what one needs to measure in order to determine the magnitude of the cyclic displacement error. The precision attainable in the conventional (phase measurement) approach to measuring cyclic error is limited because the phase measurements are af-

Atmospheric pressure loading effects on Global Positioning System coordinate determinations

NASA Technical Reports Server (NTRS)

Vandam, Tonie M.; Blewitt, Geoffrey; Heflin, Michael B.

1994-01-01

Earth deformation signals caused by atmospheric pressure loading are detected in vertical position estimates at Global Positioning System (GPS) stations. Surface displacements due to changes in atmospheric pressure account for up to 24% of the total variance in the GPS height estimates. The detected loading signals are larger at higher latitudes where pressure variations are greatest; the largest effect is observed at Fairbanks, Alaska (latitude 65 deg), with a signal root mean square (RMS) of 5 mm. Out of 19 continuously operating GPS sites (with a mean of 281 daily solutions per site), 18 show a positive correlation between the GPS vertical estimates and the modeled loading displacements. Accounting for loading reduces the variance of the vertical station positions on 12 of the 19 sites investigated. Removing the modeled pressure loading from GPS determinations of baseline length for baselines longer than 6000 km reduces the variance on 73 of the 117 baselines investigated. The slight increase in variance for some of the sites and baselines is consistent with expected statistical fluctuations. The results from most stations are consistent with approximately 65% of the modeled pressure load being found in the GPS vertical position measurements. Removing an annual signal from both the measured heights and the modeled load time series leaves this value unchanged. The source of the remaining discrepancy between the modeled and observed loading signal may be the result of (1) anisotropic effects in the Earth's loading response, (2) errors in GPS estimates of tropospheric delay, (3) errors in the surface pressure data, or (4) annual signals in the time series of loading and station heights. In addition, we find that using site dependent coefficients, determined by fitting local pressure to the modeled radial displacements, reduces the variance of the measured station heights as well as or better than using the global convolution sum.

Improved human observer performance in digital reconstructed radiograph verification in head and neck cancer radiotherapy.

PubMed

Sturgeon, Jared D; Cox, John A; Mayo, Lauren L; Gunn, G Brandon; Zhang, Lifei; Balter, Peter A; Dong, Lei; Awan, Musaddiq; Kocak-Uzel, Esengul; Mohamed, Abdallah Sherif Radwan; Rosenthal, David I; Fuller, Clifton David

2015-10-01

Digitally reconstructed radiographs (DRRs) are routinely used as an a priori reference for setup correction in radiotherapy. The spatial resolution of DRRs may be improved to reduce setup error in fractionated radiotherapy treatment protocols. The influence of finer CT slice thickness reconstruction (STR) and resultant increased resolution DRRs on physician setup accuracy was prospectively evaluated. Four head and neck patient CT-simulation images were acquired and used to create DRR cohorts by varying STRs at 0.5, 1, 2, 2.5, and 3 mm. DRRs were displaced relative to a fixed isocenter using 0-5 mm random shifts in the three cardinal axes. Physician observers reviewed DRRs of varying STRs and displacements and then aligned reference and test DRRs replicating daily KV imaging workflow. A total of 1,064 images were reviewed by four blinded physicians. Observer errors were analyzed using nonparametric statistics (Friedman's test) to determine whether STR cohorts had detectably different displacement profiles. Post hoc bootstrap resampling was applied to evaluate potential generalizability. The observer-based trial revealed a statistically significant difference between cohort means for observer displacement vector error ([Formula: see text]) and for [Formula: see text]-axis [Formula: see text]. Bootstrap analysis suggests a 15% gain in isocenter translational setup error with reduction of STR from 3 mm to [Formula: see text]2 mm, though interobserver variance was a larger feature than STR-associated measurement variance. Higher resolution DRRs generated using finer CT scan STR resulted in improved observer performance at shift detection and could decrease operator-dependent geometric error. Ideally, CT STRs [Formula: see text]2 mm should be utilized for DRR generation in the head and neck.

Improved accuracy of ultrasound-guided therapies using electromagnetic tracking: in-vivo speed of sound measurements

NASA Astrophysics Data System (ADS)

Samboju, Vishal; Adams, Matthew; Salgaonkar, Vasant; Diederich, Chris J.; Cunha, J. Adam M.

2017-02-01

The speed of sound (SOS) for ultrasound devices used for imaging soft tissue is often calibrated to water, 1540 m/s1 , despite in-vivo soft tissue SOS varying from 1450 to 1613 m/s2 . Images acquired with 1540 m/s and used in conjunction with stereotactic external coordinate systems can thus result in displacement errors of several millimeters. Ultrasound imaging systems are routinely used to guide interventional thermal ablation and cryoablation devices, or radiation sources for brachytherapy3 . Brachytherapy uses small radioactive pellets, inserted interstitially with needles under ultrasound guidance, to eradicate cancerous tissue4 . Since the radiation dose diminishes with distance from the pellet as 1/r2 , imaging uncertainty of a few millimeters can result in significant erroneous dose delivery5,6. Likewise, modeling of power deposition and thermal dose accumulations from ablative sources are also prone to errors due to placement offsets from SOS errors7 . This work presents a method of mitigating needle placement error due to SOS variances without the need of ionizing radiation2,8. We demonstrate the effects of changes in dosimetry in a prostate brachytherapy environment due to patientspecific SOS variances and the ability to mitigate dose delivery uncertainty. Electromagnetic (EM) sensors embedded in the brachytherapy ultrasound system provide information regarding 3D position and orientation of the ultrasound array. Algorithms using data from these two modalities are used to correct bmode images to account for SOS errors. While ultrasound localization resulted in >3 mm displacements, EM resolution was verified to <1 mm precision using custom-built phantoms with various SOS, showing 1% accuracy in SOS measurement.

Integrating models that depend on variable data

NASA Astrophysics Data System (ADS)

Banks, A. T.; Hill, M. C.

2016-12-01

Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log-transformation. Greater consistency is obtained by imposing smaller (by up to a factor of 1/35) weights on the smaller dependent-variable values. From an error-based perspective, the small weights are consistent with large standard deviations. This work considers the consequences of these two common ways of addressing variable data.

Performance of chromatographic systems to model soil-water sorption.

PubMed

Hidalgo-Rodríguez, Marta; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2012-08-24

A systematic approach for evaluating the goodness of chromatographic systems to model the sorption of neutral organic compounds by soil from water is presented in this work. It is based on the examination of the three sources of error that determine the overall variance obtained when soil-water partition coefficients are correlated against chromatographic retention factors: the variance of the soil-water sorption data, the variance of the chromatographic data, and the variance attributed to the dissimilarity between the two systems. These contributions of variance are easily predicted through the characterization of the systems by the solvation parameter model. According to this method, several chromatographic systems besides the reference octanol-water partition system have been selected to test their performance in the emulation of soil-water sorption. The results from the experimental correlations agree with the predicted variances. The high-performance liquid chromatography system based on an immobilized artificial membrane and the micellar electrokinetic chromatography systems of sodium dodecylsulfate and sodium taurocholate provide the most precise correlation models. They have shown to predict well soil-water sorption coefficients of several tested herbicides. Octanol-water partitions and high-performance liquid chromatography measurements using C18 columns are less suited for the estimation of soil-water partition coefficients. Copyright © 2012 Elsevier B.V. All rights reserved.

Analysis of the PLL phase error in presence of simulated ionospheric scintillation events

NASA Astrophysics Data System (ADS)

Forte, B.

2012-01-01

The functioning of standard phase locked loops (PLL), including those used to track radio signals from Global Navigation Satellite Systems (GNSS), is based on a linear approximation which holds in presence of small phase errors. Such an approximation represents a reasonable assumption in most of the propagation channels. However, in presence of a fading channel the phase error may become large, making the linear approximation no longer valid. The PLL is then expected to operate in a non-linear regime. As PLLs are generally designed and expected to operate in their linear regime, whenever the non-linear regime comes into play, they will experience a serious limitation in their capability to track the corresponding signals. The phase error and the performance of a typical PLL embedded into a commercial multiconstellation GNSS receiver were analyzed in presence of simulated ionospheric scintillation. Large phase errors occurred during scintillation-induced signal fluctuations although cycle slips only occurred during the signal re-acquisition after a loss of lock. Losses of lock occurred whenever the signal faded below the minimumC/N0threshold allowed for tracking. The simulations were performed for different signals (GPS L1C/A, GPS L2C, GPS L5 and Galileo L1). L5 and L2C proved to be weaker than L1. It appeared evident that the conditions driving the PLL phase error in the specific case of GPS receivers in presence of scintillation-induced signal perturbations need to be evaluated in terms of the combination of the minimumC/N0 tracking threshold, lock detector thresholds, possible cycle slips in the tracking PLL and accuracy of the observables (i.e. the error propagation onto the observables stage).

Addressing Phase Errors in Fat-Water Imaging Using a Mixed Magnitude/Complex Fitting Method

PubMed Central

Hernando, D.; Hines, C. D. G.; Yu, H.; Reeder, S.B.

2012-01-01

Accurate, noninvasive measurements of liver fat content are needed for the early diagnosis and quantitative staging of nonalcoholic fatty liver disease. Chemical shift-based fat quantification methods acquire images at multiple echo times using a multiecho spoiled gradient echo sequence, and provide fat fraction measurements through postprocessing. However, phase errors, such as those caused by eddy currents, can adversely affect fat quantification. These phase errors are typically most significant at the first echo of the echo train, and introduce bias in complex-based fat quantification techniques. These errors can be overcome using a magnitude-based technique (where the phase of all echoes is discarded), but at the cost of significantly degraded signal-to-noise ratio, particularly for certain choices of echo time combinations. In this work, we develop a reconstruction method that overcomes these phase errors without the signal-to-noise ratio penalty incurred by magnitude fitting. This method discards the phase of the first echo (which is often corrupted) while maintaining the phase of the remaining echoes (where phase is unaltered). We test the proposed method on 104 patient liver datasets (from 52 patients, each scanned twice), where the fat fraction measurements are compared to coregistered spectroscopy measurements. We demonstrate that mixed fitting is able to provide accurate fat fraction measurements with high signal-to-noise ratio and low bias over a wide choice of echo combinations. PMID:21713978

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

Temperature-fluctuation-sensitive accumulative effect of the phase measurement errors in low-coherence interferometry in characterizing arrayed waveguide gratings.

PubMed

Zhao, Changyun; Wei, Bing; Yang, Longzhi; Wang, Gencheng; Wang, Yuehai; Jiang, Xiaoqing; Li, Yubo; Yang, Jianyi

2015-09-20

We investigate the accumulative effect of the phase measurement errors in characterizing optical multipath components by low-coherence interferometry. The accumulative effect is caused by the fluctuation of the environment temperature, which leads to the variation of the refractive index of the device under test. The resulting phase measurement errors accumulate with the increasing of the phase difference between the two interferometer arms. Our experiments were carried out to demonstrate that the accumulative effect is still obvious even though the thermo-optical coefficient of the device under test is quite small. Shortening the measurement time to reduce the fluctuation of the environment temperature can effectively restrain the accumulative effect. The experiments show that when the scanning speed increases to 4.8 mm/s, the slope of the phase measurement errors decreases to 5.52×10(-8), which means the accumulative effect can be ignored.

The Influence of Training Phase on Error of Measurement in Jump Performance.

PubMed

Taylor, Kristie-Lee; Hopkins, Will G; Chapman, Dale W; Cronin, John B

2016-03-01

The purpose of this study was to calculate the coefficients of variation in jump performance for individual participants in multiple trials over time to determine the extent to which there are real differences in the error of measurement between participants. The effect of training phase on measurement error was also investigated. Six subjects participated in a resistance-training intervention for 12 wk with mean power from a countermovement jump measured 6 d/wk. Using a mixed-model meta-analysis, differences between subjects, within-subject changes between training phases, and the mean error values during different phases of training were examined. Small, substantial factor differences of 1.11 were observed between subjects; however, the finding was unclear based on the width of the confidence limits. The mean error was clearly higher during overload training than baseline training, by a factor of ×/÷ 1.3 (confidence limits 1.0-1.6). The random factor representing the interaction between subjects and training phases revealed further substantial differences of ×/÷ 1.2 (1.1-1.3), indicating that on average, the error of measurement in some subjects changes more than in others when overload training is introduced. The results from this study provide the first indication that within-subject variability in performance is substantially different between training phases and, possibly, different between individuals. The implications of these findings for monitoring individuals and estimating sample size are discussed.

Latin-square three-dimensional gage master

DOEpatents

Jones, L.

1981-05-12

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

Latin square three dimensional gage master

DOEpatents

Jones, Lynn L.

1982-01-01

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

Covariate Imbalance and Precision in Measuring Treatment Effects

ERIC Educational Resources Information Center

Liu, Xiaofeng Steven

2011-01-01

Covariate adjustment can increase the precision of estimates by removing unexplained variance from the error in randomized experiments, although chance covariate imbalance tends to counteract the improvement in precision. The author develops an easy measure to examine chance covariate imbalance in randomization by standardizing the average…

Bayesian Meta-Analysis of Coefficient Alpha

ERIC Educational Resources Information Center

Brannick, Michael T.; Zhang, Nanhua

2013-01-01

The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…

A new statistic to express the uncertainty of kriging predictions for purposes of survey planning.

NASA Astrophysics Data System (ADS)

Lark, R. M.; Lapworth, D. J.

2014-05-01

It is well-known that one advantage of kriging for spatial prediction is that, given the random effects model, the prediction error variance can be computed a priori for alternative sampling designs. This allows one to compare sampling schemes, in particular sampling at different densities, and so to decide on one which meets requirements in terms of the uncertainty of the resulting predictions. However, the planning of sampling schemes must account not only for statistical considerations, but also logistics and cost. This requires effective communication between statisticians, soil scientists and data users/sponsors such as managers, regulators or civil servants. In our experience the latter parties are not necessarily able to interpret the prediction error variance as a measure of uncertainty for decision making. In some contexts (particularly the solution of very specific problems at large cartographic scales, e.g. site remediation and precision farming) it is possible to translate uncertainty of predictions into a loss function directly comparable with the cost incurred in increasing precision. Often, however, sampling must be planned for more generic purposes (e.g. baseline or exploratory geochemical surveys). In this latter context the prediction error variance may be of limited value to a non-statistician who has to make a decision on sample intensity and associated cost. We propose an alternative criterion for these circumstances to aid communication between statisticians and data users about the uncertainty of geostatistical surveys based on different sampling intensities. The criterion is the consistency of estimates made from two non-coincident instantiations of a proposed sample design. We consider square sample grids, one instantiation is offset from the second by half the grid spacing along the rows and along the columns. If a sample grid is coarse relative to the important scales of variation in the target property then the consistency of predictions from two instantiations is expected to be small, and can be increased by reducing the grid spacing. The measure of consistency is the correlation between estimates from the two instantiations of the sample grid, averaged over a grid cell. We call this the offset correlation, it can be calculated from the variogram. We propose that this measure is easier to grasp intuitively than the prediction error variance, and has the advantage of having an upper bound (1.0) which will aid its interpretation. This quality measure is illustrated for some hypothetical examples, considering both ordinary kriging and factorial kriging of the variable of interest. It is also illustrated using data on metal concentrations in the soil of north-east England.

Measuring kinetics of complex single ion channel data using mean-variance histograms.

PubMed Central

Patlak, J B

1993-01-01

The measurement of single ion channel kinetics is difficult when those channels exhibit subconductance events. When the kinetics are fast, and when the current magnitudes are small, as is the case for Na+, Ca2+, and some K+ channels, these difficulties can lead to serious errors in the estimation of channel kinetics. I present here a method, based on the construction and analysis of mean-variance histograms, that can overcome these problems. A mean-variance histogram is constructed by calculating the mean current and the current variance within a brief "window" (a set of N consecutive data samples) superimposed on the digitized raw channel data. Systematic movement of this window over the data produces large numbers of mean-variance pairs which can be assembled into a two-dimensional histogram. Defined current levels (open, closed, or sublevel) appear in such plots as low variance regions. The total number of events in such low variance regions is estimated by curve fitting and plotted as a function of window width. This function decreases with the same time constants as the original dwell time probability distribution for each of the regions. The method can therefore be used: 1) to present a qualitative summary of the single channel data from which the signal-to-noise ratio, open channel noise, steadiness of the baseline, and number of conductance levels can be quickly determined; 2) to quantify the dwell time distribution in each of the levels exhibited. In this paper I present the analysis of a Na+ channel recording that had a number of complexities. The signal-to-noise ratio was only about 8 for the main open state, open channel noise, and fast flickers to other states were present, as were a substantial number of subconductance states. "Standard" half-amplitude threshold analysis of these data produce open and closed time histograms that were well fitted by the sum of two exponentials, but with apparently erroneous time constants, whereas the mean-variance histogram technique provided a more credible analysis of the open, closed, and subconductance times for the patch. I also show that the method produces accurate results on simulated data in a wide variety of conditions, whereas the half-amplitude method, when applied to complex simulated data shows the same errors as were apparent in the real data. The utility and the limitations of this new method are discussed. Images FIGURE 2 FIGURE 4 FIGURE 8 FIGURE 9 PMID:7690261

Adaptive Prior Variance Calibration in the Bayesian Continual Reassessment Method

PubMed Central

Zhang, Jin; Braun, Thomas M.; Taylor, Jeremy M.G.

2012-01-01

Use of the Continual Reassessment Method (CRM) and other model-based approaches to design in Phase I clinical trials has increased due to the ability of the CRM to identify the maximum tolerated dose (MTD) better than the 3+3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. While methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation to methods proposed to calibrate the variance at the beginning of a trial. PMID:22987660

SU-E-T-56: A Novel Approach to Computing Expected Value and Variance of Point Dose From Non-Gated Radiotherapy Delivery

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

Zhou, S; Zhu, X; Zhang, M

Purpose: Randomness in patient internal organ motion phase at the beginning of non-gated radiotherapy delivery may introduce uncertainty to dose received by the patient. Concerns of this dose deviation from the planned one has motivated many researchers to study this phenomenon although unified theoretical framework for computing it is still missing. This study was conducted to develop such framework for analyzing the effect. Methods: Two reasonable assumptions were made: a) patient internal organ motion is stationary and periodic; b) no special arrangement is made to start a non -gated radiotherapy delivery at any specific phase of patient internal organ motion.more » A statistical ensemble was formed consisting of patient’s non-gated radiotherapy deliveries at all equally possible initial organ motion phases. To characterize the patient received dose, statistical ensemble average method is employed to derive formulae for two variables: expected value and variance of dose received by a patient internal point from a non-gated radiotherapy delivery. Fourier Series was utilized to facilitate our analysis. Results: According to our formulae, the two variables can be computed from non-gated radiotherapy generated dose rate time sequences at the point’s corresponding locations on fixed phase 3D CT images sampled evenly in time over one patient internal organ motion period. The expected value of point dose is simply the average of the doses to the point’s corresponding locations on the fixed phase CT images. The variance can be determined by time integration in terms of Fourier Series coefficients of the dose rate time sequences on the same fixed phase 3D CT images. Conclusion: Given a non-gated radiotherapy delivery plan and patient’s 4D CT study, our novel approach can predict the expected value and variance of patient radiation dose. We expect it to play a significant role in determining both quality and robustness of patient non-gated radiotherapy plan.« less

Testing the factor structure of the Family Quality of Life Survey - 2006.

PubMed

Isaacs, B; Wang, M; Samuel, P; Ajuwon, P; Baum, N; Edwards, M; Rillotta, F

2012-01-01

Although the Family Quality of Life Survey - 2006 (FQOLS-2006) is being used in research, there is little evidence to support its hypothesised domain structure. The purpose of this study was to test the domain structure of the survey using confirmatory factor analysis. Samples from Australia, Canada, Nigeria and the USA were analysed using structural equation modelling. The data from Australia, Canada and the USA were combined on the assumption that these countries are similar, at least to some degree, in economic development, language and culture. The Nigerian data were analysed on its own. The analysis was undertaken in two phases. First, the hypothesis that each of nine domains of the FQOLS-2006 is a unidimensional construct that can reliably measure the dimensions Importance, Stability, Opportunities, Attainment, Stability and Satisfaction was tested. Second, the hypothesis that family quality of life (FQoL) is a single latent construct represented by the nine domains measured in the FQOLS-2006 was tested. In the first phase of the analysis, the Importance dimension was dropped because of skewness and lack of variance. The Stability dimension did not fit well within the individual domain model in both the Nigerian and the combined three countries' data. When Importance and Stability were excluded, the individual domain models showede good or acceptable fit when error variances of some dimensions were allowed to correlate. In the second phase of the analysis, the overall model, FQoL, represented by the nine domains of the FQOLS-2006 showed good fit in both data sets. The conceptual model of the FQOLS-2006 was supported with some qualifications. Each domain on the survey can be reliably measured by four dimensions Opportunities, Initiative, Attainment and Satisfaction. The dimensions of Importance and Stability, however, did not fit. Data reported on these dimensions from past and current studies should be interpreted with caution. The construct of FQoL is also reliably measured by the domains of the FQOLS-2006. Further research into the psychometric properties of the survey, particularly from a cross-cultural perspective, is needed. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

Using heuristic evaluations to assess the safety of health information systems.

PubMed

Carvalho, Christopher J; Borycki, Elizabeth M; Kushniruk, Andre W

2009-01-01

Health information systems (HISs) are typically seen as a mechanism for reducing medical errors. There is, however, evidence to prove that technology may actually be the cause of errors. As a result, it is crucial to fully test any system prior to its implementation. At present, evidence-based evaluation heuristics do not exist for assessing aspects of interface design that lead to medical errors. A three phase study was conducted to develop evidence-based heuristics for evaluating interfaces. Phase 1 consisted of a systematic review of the literature. In Phase 2 a comprehensive list of 33 evaluation heuristics was developed based on the review that could be used to test for potential technology induced errors. Phase 3 involved applying these healthcare specific heuristics to evaluate a HIS.

A Six Sigma Trial For Reduction of Error Rates in Pathology Laboratory.

PubMed

Tosuner, Zeynep; Gücin, Zühal; Kiran, Tuğçe; Büyükpinarbaşili, Nur; Turna, Seval; Taşkiran, Olcay; Arici, Dilek Sema

2016-01-01

A major target of quality assurance is the minimization of error rates in order to enhance patient safety. Six Sigma is a method targeting zero error (3.4 errors per million events) used in industry. The five main principles of Six Sigma are defining, measuring, analysis, improvement and control. Using this methodology, the causes of errors can be examined and process improvement strategies can be identified. The aim of our study was to evaluate the utility of Six Sigma methodology in error reduction in our pathology laboratory. The errors encountered between April 2014 and April 2015 were recorded by the pathology personnel. Error follow-up forms were examined by the quality control supervisor, administrative supervisor and the head of the department. Using Six Sigma methodology, the rate of errors was measured monthly and the distribution of errors at the preanalytic, analytic and postanalytical phases was analysed. Improvement strategies were reclaimed in the monthly intradepartmental meetings and the control of the units with high error rates was provided. Fifty-six (52.4%) of 107 recorded errors in total were at the pre-analytic phase. Forty-five errors (42%) were recorded as analytical and 6 errors (5.6%) as post-analytical. Two of the 45 errors were major irrevocable errors. The error rate was 6.8 per million in the first half of the year and 1.3 per million in the second half, decreasing by 79.77%. The Six Sigma trial in our pathology laboratory provided the reduction of the error rates mainly in the pre-analytic and analytic phases.

Threshold detection in an on-off binary communications channel with atmospheric scintillation

NASA Technical Reports Server (NTRS)

Webb, W. E.; Marino, J. T., Jr.

1974-01-01

The optimum detection threshold in an on-off binary optical communications system operating in the presence of atmospheric turbulence was investigated assuming a poisson detection process and log normal scintillation. The dependence of the probability of bit error on log amplitude variance and received signal strength was analyzed and semi-emperical relationships to predict the optimum detection threshold derived. On the basis of this analysis a piecewise linear model for an adaptive threshold detection system is presented. Bit error probabilities for non-optimum threshold detection system were also investigated.

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.

Threshold detection in an on-off binary communications channel with atmospheric scintillation

NASA Technical Reports Server (NTRS)

Webb, W. E.

1975-01-01

The optimum detection threshold in an on-off binary optical communications system operating in the presence of atmospheric turbulence was investigated assuming a poisson detection process and log normal scintillation. The dependence of the probability of bit error on log amplitude variance and received signal strength was analyzed and semi-empirical relationships to predict the optimum detection threshold derived. On the basis of this analysis a piecewise linear model for an adaptive threshold detection system is presented. The bit error probabilities for nonoptimum threshold detection systems were also investigated.

Interferometric phase measurement techniques for coherent beam combining

NASA Astrophysics Data System (ADS)

Antier, Marie; Bourderionnet, Jérôme; Larat, Christian; Lallier, Eric; Primot, Jérôme; Brignon, Arnaud

2015-03-01

Coherent beam combining of fiber amplifiers provides an attractive mean of reaching high power laser. In an interferometric phase measurement the beams issued for each fiber combined are imaged onto a sensor and interfere with a reference plane wave. This registration of interference patterns on a camera allows the measurement of the exact phase error of each fiber beam in a single shot. Therefore, this method is a promising candidate toward very large number of combined fibers. Based on this technique, several architectures can be proposed to coherently combine a high number of fibers. The first one based on digital holography transfers directly the image of the camera to spatial light modulator (SLM). The generated hologram is used to compensate the phase errors induced by the amplifiers. This architecture has therefore a collective phase measurement and correction. Unlike previous digital holography technique, the probe beams measuring the phase errors between the fibers are co-propagating with the phase-locked signal beams. This architecture is compatible with the use of multi-stage isolated amplifying fibers. In that case, only 20 pixels per fiber on the SLM are needed to obtain a residual phase shift error below λ/10rms. The second proposed architecture calculates the correction applied to each fiber channel by tracking the relative position of the interference finges. In this case, a phase modulator is placed on each channel. In that configuration, only 8 pixels per fiber on the camera is required for a stable close loop operation with a residual phase error of λ/20rms, which demonstrates the scalability of this concept.

Advances in the meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model.

PubMed

Doi, Suhail A R; Barendregt, Jan J; Khan, Shahjahan; Thalib, Lukman; Williams, Gail M

2015-11-01

This article examines an improved alternative to the random effects (RE) model for meta-analysis of heterogeneous studies. It is shown that the known issues of underestimation of the statistical error and spuriously overconfident estimates with the RE model can be resolved by the use of an estimator under the fixed effect model assumption with a quasi-likelihood based variance structure - the IVhet model. Extensive simulations confirm that this estimator retains a correct coverage probability and a lower observed variance than the RE model estimator, regardless of heterogeneity. When the proposed IVhet method is applied to the controversial meta-analysis of intravenous magnesium for the prevention of mortality after myocardial infarction, the pooled OR is 1.01 (95% CI 0.71-1.46) which not only favors the larger studies but also indicates more uncertainty around the point estimate. In comparison, under the RE model the pooled OR is 0.71 (95% CI 0.57-0.89) which, given the simulation results, reflects underestimation of the statistical error. Given the compelling evidence generated, we recommend that the IVhet model replace both the FE and RE models. To facilitate this, it has been implemented into free meta-analysis software called MetaXL which can be downloaded from www.epigear.com. Copyright © 2015 Elsevier Inc. All rights reserved.

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

NASA Astrophysics Data System (ADS)

Maginnis, P. A.; West, M.; Dullerud, G. E.

2016-10-01

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a ;black-box;, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

An active co-phasing imaging testbed with segmented mirrors

NASA Astrophysics Data System (ADS)

Zhao, Weirui; Cao, Genrui

2011-06-01

An active co-phasing imaging testbed with high accurate optical adjustment and control in nanometer scale was set up to validate the algorithms of piston and tip-tilt error sensing and real-time adjusting. Modularization design was adopted. The primary mirror was spherical and divided into three sub-mirrors. One of them was fixed and worked as reference segment, the others were adjustable respectively related to the fixed segment in three freedoms (piston, tip and tilt) by using sensitive micro-displacement actuators in the range of 15mm with a resolution of 3nm. The method of twodimension dispersed fringe analysis was used to sense the piston error between the adjacent segments in the range of 200μm with a repeatability of 2nm. And the tip-tilt error was gained with the method of centroid sensing. Co-phasing image could be realized by correcting the errors measured above with the sensitive micro-displacement actuators driven by a computer. The process of co-phasing error sensing and correcting could be monitored in real time by a scrutiny module set in this testbed. A FISBA interferometer was introduced to evaluate the co-phasing performance, and finally a total residual surface error of about 50nm rms was achieved.

Fusing metabolomics data sets with heterogeneous measurement errors

PubMed Central

Waaijenborg, Sandra; Korobko, Oksana; Willems van Dijk, Ko; Lips, Mirjam; Hankemeier, Thomas; Wilderjans, Tom F.; Smilde, Age K.

2018-01-01

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups. PMID:29698490

Response Monitoring and Adjustment: Differential Relations with Psychopathic Traits

PubMed Central

Bresin, Konrad; Finy, M. Sima; Sprague, Jenessa; Verona, Edelyn

2014-01-01

Studies on the relation between psychopathy and cognitive functioning often show mixed results, partially because different factors of psychopathy have not been considered fully. Based on previous research, we predicted divergent results based on a two-factor model of psychopathy (interpersonal-affective traits and impulsive-antisocial traits). Specifically, we predicted that the unique variance of interpersonal-affective traits would be related to increased monitoring (i.e., error-related negativity) and adjusting to errors (i.e., post-error slowing), whereas impulsive-antisocial traits would be related to reductions in these processes. Three studies using a diverse selection of assessment tools, samples, and methods are presented to identify response monitoring correlates of the two main factors of psychopathy. In Studies 1 (undergraduates), 2 (adolescents), and 3 (offenders), interpersonal-affective traits were related to increased adjustment following errors and, in Study 3, to enhanced monitoring of errors. Impulsive-antisocial traits were not consistently related to error adjustment across the studies, although these traits were related to a deficient monitoring of errors in Study 3. The results may help explain previous mixed findings and advance implications for etiological models of psychopathy. PMID:24933282

Performance Analysis of Amplify-and-Forward Relaying FSO/SC-QAM Systems over Weak Turbulence Channels and Pointing Error Impairments

NASA Astrophysics Data System (ADS)

Trung, Ha Duyen

2017-12-01

In this paper, the end-to-end performance of free-space optical (FSO) communication system combining with Amplify-and-Forward (AF)-assisted or fixed-gain relaying technology using subcarrier quadrature amplitude modulation (SC-QAM) over weak atmospheric turbulence channels modeled by log-normal distribution with pointing error impairments is studied. More specifically, unlike previous studies on AF relaying FSO communication systems without pointing error effects; the pointing error effect is studied by taking into account the influence of beamwidth, aperture size and jitter variance. In addition, a combination of these models to analyze the combined effect of atmospheric turbulence and pointing error to AF relaying FSO/SC-QAM systems is used. Finally, an analytical expression is derived to evaluate the average symbol error rate (ASER) performance of such systems. The numerical results show that the impact of pointing error on the performance of AF relaying FSO/SC-QAM systems and how we use proper values of aperture size and beamwidth to improve the performance of such systems. Some analytical results are confirmed by Monte-Carlo simulations.

Factors affecting nursing students' intention to report medication errors: An application of the theory of planned behavior.

PubMed

Ben Natan, Merav; Sharon, Ira; Mahajna, Marlen; Mahajna, Sara

2017-11-01

Medication errors are common among nursing students. Nonetheless, these errors are often underreported. To examine factors related to nursing students' intention to report medication errors, using the Theory of Planned Behavior, and to examine whether the theory is useful in predicting students' intention to report errors. This study has a descriptive cross-sectional design. Study population was recruited in a university and a large nursing school in central and northern Israel. A convenience sample of 250 nursing students took part in the study. The students completed a self-report questionnaire, based on the Theory of Planned Behavior. The findings indicate that students' intention to report medication errors was high. The Theory of Planned Behavior constructs explained 38% of variance in students' intention to report medication errors. The constructs of behavioral beliefs, subjective norms, and perceived behavioral control were found as affecting this intention, while the most significant factor was behavioral beliefs. The findings also reveal that students' fear of the reaction to disclosure of the error from superiors and colleagues may impede them from reporting the error. Understanding factors related to reporting medication errors is crucial to designing interventions that foster error reporting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating means and variances: The comparative efficiency of composite and grab samples.

PubMed

Brumelle, S; Nemetz, P; Casey, D

1984-03-01

This paper compares the efficiencies of two sampling techniques for estimating a population mean and variance. One procedure, called grab sampling, consists of collecting and analyzing one sample per period. The second procedure, called composite sampling, collectsn samples per period which are then pooled and analyzed as a single sample. We review the well known fact that composite sampling provides a superior estimate of the mean. However, it is somewhat surprising that composite sampling does not always generate a more efficient estimate of the variance. For populations with platykurtic distributions, grab sampling gives a more efficient estimate of the variance, whereas composite sampling is better for leptokurtic distributions. These conditions on kurtosis can be related to peakedness and skewness. For example, a necessary condition for composite sampling to provide a more efficient estimate of the variance is that the population density function evaluated at the mean (i.e.f(μ)) be greater than[Formula: see text]. If[Formula: see text], then a grab sample is more efficient. In spite of this result, however, composite sampling does provide a smaller estimate of standard error than does grab sampling in the context of estimating population means.