Superfast maximum-likelihood reconstruction for quantum tomography

NASA Astrophysics Data System (ADS)

Shang, Jiangwei; Zhang, Zhengyun; Ng, Hui Khoon

2017-06-01

Conventional methods for computing maximum-likelihood estimators (MLE) often converge slowly in practical situations, leading to a search for simplifying methods that rely on additional assumptions for their validity. In this work, we provide a fast and reliable algorithm for maximum-likelihood reconstruction that avoids this slow convergence. Our method utilizes the state-of-the-art convex optimization scheme, an accelerated projected-gradient method, that allows one to accommodate the quantum nature of the problem in a different way than in the standard methods. We demonstrate the power of our approach by comparing its performance with other algorithms for n -qubit state tomography. In particular, an eight-qubit situation that purportedly took weeks of computation time in 2005 can now be completed in under a minute for a single set of data, with far higher accuracy than previously possible. This refutes the common claim that MLE reconstruction is slow and reduces the need for alternative methods that often come with difficult-to-verify assumptions. In fact, recent methods assuming Gaussian statistics or relying on compressed sensing ideas are demonstrably inapplicable for the situation under consideration here. Our algorithm can be applied to general optimization problems over the quantum state space; the philosophy of projected gradients can further be utilized for optimization contexts with general constraints.

Maximum likelihood clustering with dependent feature trees

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1981-01-01

The decomposition of mixture density of the data into its normal component densities is considered. The densities are approximated with first order dependent feature trees using criteria of mutual information and distance measures. Expressions are presented for the criteria when the densities are Gaussian. By defining different typs of nodes in a general dependent feature tree, maximum likelihood equations are developed for the estimation of parameters using fixed point iterations. The field structure of the data is also taken into account in developing maximum likelihood equations. Experimental results from the processing of remotely sensed multispectral scanner imagery data are included.

The Maximum Likelihood Solution for Inclination-only Data

NASA Astrophysics Data System (ADS)

Arason, P.; Levi, S.

2006-12-01

The arithmetic means of inclination-only data are known to introduce a shallowing bias. Several methods have been proposed to estimate unbiased means of the inclination along with measures of the precision. Most of the inclination-only methods were designed to maximize the likelihood function of the marginal Fisher distribution. However, the exact analytical form of the maximum likelihood function is fairly complicated, and all these methods require various assumptions and approximations that are inappropriate for many data sets. For some steep and dispersed data sets, the estimates provided by these methods are significantly displaced from the peak of the likelihood function to systematically shallower inclinations. The problem in locating the maximum of the likelihood function is partly due to difficulties in accurately evaluating the function for all values of interest. This is because some elements of the log-likelihood function increase exponentially as precision parameters increase, leading to numerical instabilities. In this study we succeeded in analytically cancelling exponential elements from the likelihood function, and we are now able to calculate its value for any location in the parameter space and for any inclination-only data set, with full accuracy. Furtermore, we can now calculate the partial derivatives of the likelihood function with desired accuracy. Locating the maximum likelihood without the assumptions required by previous methods is now straight forward. The information to separate the mean inclination from the precision parameter will be lost for very steep and dispersed data sets. It is worth noting that the likelihood function always has a maximum value. However, for some dispersed and steep data sets with few samples, the likelihood function takes its highest value on the boundary of the parameter space, i.e. at inclinations of +/- 90 degrees, but with relatively well defined dispersion. Our simulations indicate that this occurs quite

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen; Wald, Lawrence L.

2017-01-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization. PMID:26915119

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

PubMed

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L

2016-08-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

Maximum Likelihood Estimations and EM Algorithms with Length-biased Data

PubMed Central

Qin, Jing; Ning, Jing; Liu, Hao; Shen, Yu

2012-01-01

SUMMARY Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, epidemiological, genetic and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimations and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for estimations based on full likelihoods involving infinite dimensional parameters under three settings for length-biased data: estimating nonparametric distribution function, estimating nonparametric hazard function under an increasing failure rate constraint, and jointly estimating baseline hazards function and the covariate coefficients under the Cox proportional hazards model. Extensive empirical simulation studies show that the maximum likelihood estimators perform well with moderate sample sizes and lead to more efficient estimators compared to the estimating equation approaches. The proposed estimates are also more robust to various right-censoring mechanisms. We prove the strong consistency properties of the estimators, and establish the asymptotic normality of the semi-parametric maximum likelihood estimators under the Cox model using modern empirical processes theory. We apply the proposed methods to a prevalent cohort medical study. Supplemental materials are available online. PMID:22323840

MXLKID: a maximum likelihood parameter identifier. [In LRLTRAN for CDC 7600

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

Gavel, D.T.

MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables.

Maximum likelihood estimation of finite mixture model for economic data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2005-01-01

In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Klein, Vladislav

1994-01-01

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

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

PubMed

Scheike, Thomas H; Juul, Anders

2004-04-01

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

An evaluation of several different classification schemes - Their parameters and performance. [maximum likelihood decision for crop identification

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.

1979-01-01

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

NASA Technical Reports Server (NTRS)

Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

1990-01-01

An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes

PubMed Central

Chen, Rui; Hyrien, Ollivier

2011-01-01

This article deals with quasi- and pseudo-likelihood estimation in a class of continuous-time multi-type Markov branching processes observed at discrete points in time. “Conventional” and conditional estimation are discussed for both approaches. We compare their properties and identify situations where they lead to asymptotically equivalent estimators. Both approaches possess robustness properties, and coincide with maximum likelihood estimation in some cases. Quasi-likelihood functions involving only linear combinations of the data may be unable to estimate all model parameters. Remedial measures exist, including the resort either to non-linear functions of the data or to conditioning the moments on appropriate sigma-algebras. The method of pseudo-likelihood may also resolve this issue. We investigate the properties of these approaches in three examples: the pure birth process, the linear birth-and-death process, and a two-type process that generalizes the previous two examples. Simulations studies are conducted to evaluate performance in finite samples. PMID:21552356

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Accurate Structural Correlations from Maximum Likelihood Superpositions

PubMed Central

Theobald, Douglas L; Wuttke, Deborah S

2008-01-01

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

Maximum Likelihood Estimation with Emphasis on Aircraft Flight Data

NASA Technical Reports Server (NTRS)

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

1985-01-01

Accurate modeling of flexible space structures is an important field that is currently under investigation. Parameter estimation, using methods such as maximum likelihood, is one of the ways that the model can be improved. The maximum likelihood estimator has been used to extract stability and control derivatives from flight data for many years. Most of the literature on aircraft estimation concentrates on new developments and applications, assuming familiarity with basic estimation concepts. Some of these basic concepts are presented. The maximum likelihood estimator and the aircraft equations of motion that the estimator uses are briefly discussed. The basic concepts of minimization and estimation are examined for a simple computed aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to help illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Specific examples of estimation of structural dynamics are included. Some of the major conclusions for the computed example are also developed for the analysis of flight data.

Maximum-Likelihood Methods for Processing Signals From Gamma-Ray Detectors

PubMed Central

Barrett, Harrison H.; Hunter, William C. J.; Miller, Brian William; Moore, Stephen K.; Chen, Yichun; Furenlid, Lars R.

2009-01-01

In any gamma-ray detector, each event produces electrical signals on one or more circuit elements. From these signals, we may wish to determine the presence of an interaction; whether multiple interactions occurred; the spatial coordinates in two or three dimensions of at least the primary interaction; or the total energy deposited in that interaction. We may also want to compute listmode probabilities for tomographic reconstruction. Maximum-likelihood methods provide a rigorous and in some senses optimal approach to extracting this information, and the associated Fisher information matrix provides a way of quantifying and optimizing the information conveyed by the detector. This paper will review the principles of likelihood methods as applied to gamma-ray detectors and illustrate their power with recent results from the Center for Gamma-ray Imaging. PMID:20107527

On the existence of maximum likelihood estimates for presence-only data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.

2015-01-01

It is important to identify conditions for which maximum likelihood estimates are unlikely to be identifiable from presence-only data. In data sets where the maximum likelihood estimates do not exist, penalized likelihood and Bayesian methods will produce coefficient estimates, but these are sensitive to the choice of estimation procedure and prior or penalty term. When sample size is small or it is thought that habitat preferences are strong, we propose a suite of estimation procedures researchers can consider using.

The recursive maximum likelihood proportion estimator: User's guide and test results

NASA Technical Reports Server (NTRS)

Vanrooy, D. L.

1976-01-01

Implementation of the recursive maximum likelihood proportion estimator is described. A user's guide to programs as they currently exist on the IBM 360/67 at LARS, Purdue is included, and test results on LANDSAT data are described. On Hill County data, the algorithm yields results comparable to the standard maximum likelihood proportion estimator.

New applications of maximum likelihood and Bayesian statistics in macromolecular crystallography.

PubMed

McCoy, Airlie J

2002-10-01

Maximum likelihood methods are well known to macromolecular crystallographers as the methods of choice for isomorphous phasing and structure refinement. Recently, the use of maximum likelihood and Bayesian statistics has extended to the areas of molecular replacement and density modification, placing these methods on a stronger statistical foundation and making them more accurate and effective.

Maximum-likelihood block detection of noncoherent continuous phase modulation

NASA Technical Reports Server (NTRS)

Simon, Marvin K.; Divsalar, Dariush

1993-01-01

This paper examines maximum-likelihood block detection of uncoded full response CPM over an additive white Gaussian noise (AWGN) channel. Both the maximum-likelihood metrics and the bit error probability performances of the associated detection algorithms are considered. The special and popular case of minimum-shift-keying (MSK) corresponding to h = 0.5 and constant amplitude frequency pulse is treated separately. The many new receiver structures that result from this investigation can be compared to the traditional ones that have been used in the past both from the standpoint of simplicity of implementation and optimality of performance.

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

PubMed Central

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

2008-01-01

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

Maximum likelihood estimation of signal-to-noise ratio and combiner weight

NASA Technical Reports Server (NTRS)

Kalson, S.; Dolinar, S. J.

1986-01-01

An algorithm for estimating signal to noise ratio and combiner weight parameters for a discrete time series is presented. The algorithm is based upon the joint maximum likelihood estimate of the signal and noise power. The discrete-time series are the sufficient statistics obtained after matched filtering of a biphase modulated signal in additive white Gaussian noise, before maximum likelihood decoding is performed.

A Comparison of a Bayesian and a Maximum Likelihood Tailored Testing Procedure.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A study was conducted to compare tailored testing procedures based on a Bayesian ability estimation technique and on a maximum likelihood ability estimation technique. The Bayesian tailored testing procedure selected items so as to minimize the posterior variance of the ability estimate distribution, while the maximum likelihood tailored testing…

The numerical evaluation of maximum-likelihood estimates of the parameters for a mixture of normal distributions from partially identified samples

NASA Technical Reports Server (NTRS)

Walker, H. F.

1976-01-01

Likelihood equations determined by the two types of samples which are necessary conditions for a maximum-likelihood estimate are considered. These equations, suggest certain successive-approximations iterative procedures for obtaining maximum-likelihood estimates. These are generalized steepest ascent (deflected gradient) procedures. It is shown that, with probability 1 as N sub 0 approaches infinity (regardless of the relative sizes of N sub 0 and N sub 1, i=1,...,m), these procedures converge locally to the strongly consistent maximum-likelihood estimates whenever the step size is between 0 and 2. Furthermore, the value of the step size which yields optimal local convergence rates is bounded from below by a number which always lies between 1 and 2.

Maximum-likelihood estimation of recent shared ancestry (ERSA).

PubMed

Huff, Chad D; Witherspoon, David J; Simonson, Tatum S; Xing, Jinchuan; Watkins, W Scott; Zhang, Yuhua; Tuohy, Therese M; Neklason, Deborah W; Burt, Randall W; Guthery, Stephen L; Woodward, Scott R; Jorde, Lynn B

2011-05-01

Accurate estimation of recent shared ancestry is important for genetics, evolution, medicine, conservation biology, and forensics. Established methods estimate kinship accurately for first-degree through third-degree relatives. We demonstrate that chromosomal segments shared by two individuals due to identity by descent (IBD) provide much additional information about shared ancestry. We developed a maximum-likelihood method for the estimation of recent shared ancestry (ERSA) from the number and lengths of IBD segments derived from high-density SNP or whole-genome sequence data. We used ERSA to estimate relationships from SNP genotypes in 169 individuals from three large, well-defined human pedigrees. ERSA is accurate to within one degree of relationship for 97% of first-degree through fifth-degree relatives and 80% of sixth-degree and seventh-degree relatives. We demonstrate that ERSA's statistical power approaches the maximum theoretical limit imposed by the fact that distant relatives frequently share no DNA through a common ancestor. ERSA greatly expands the range of relationships that can be estimated from genetic data and is implemented in a freely available software package.

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

Fast registration and reconstruction of aliased low-resolution frames by use of a modified maximum-likelihood approach.

PubMed

Alam, M S; Bognar, J G; Cain, S; Yasuda, B J

1998-03-10

During the process of microscanning a controlled vibrating mirror typically is used to produce subpixel shifts in a sequence of forward-looking infrared (FLIR) images. If the FLIR is mounted on a moving platform, such as an aircraft, uncontrolled random vibrations associated with the platform can be used to generate the shifts. Iterative techniques such as the expectation-maximization (EM) approach by means of the maximum-likelihood algorithm can be used to generate high-resolution images from multiple randomly shifted aliased frames. In the maximum-likelihood approach the data are considered to be Poisson random variables and an EM algorithm is developed that iteratively estimates an unaliased image that is compensated for known imager-system blur while it simultaneously estimates the translational shifts. Although this algorithm yields high-resolution images from a sequence of randomly shifted frames, it requires significant computation time and cannot be implemented for real-time applications that use the currently available high-performance processors. The new image shifts are iteratively calculated by evaluation of a cost function that compares the shifted and interlaced data frames with the corresponding values in the algorithm's latest estimate of the high-resolution image. We present a registration algorithm that estimates the shifts in one step. The shift parameters provided by the new algorithm are accurate enough to eliminate the need for iterative recalculation of translational shifts. Using this shift information, we apply a simplified version of the EM algorithm to estimate a high-resolution image from a given sequence of video frames. The proposed modified EM algorithm has been found to reduce significantly the computational burden when compared with the original EM algorithm, thus making it more attractive for practical implementation. Both simulation and experimental results are presented to verify the effectiveness of the proposed technique.

Free kick instead of cross-validation in maximum-likelihood refinement of macromolecular crystal structures

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

Pražnikar, Jure; University of Primorska,; Turk, Dušan, E-mail: dusan.turk@ijs.si

2014-12-01

The maximum-likelihood free-kick target, which calculates model error estimates from the work set and a randomly displaced model, proved superior in the accuracy and consistency of refinement of crystal structures compared with the maximum-likelihood cross-validation target, which calculates error estimates from the test set and the unperturbed model. The refinement of a molecular model is a computational procedure by which the atomic model is fitted to the diffraction data. The commonly used target in the refinement of macromolecular structures is the maximum-likelihood (ML) function, which relies on the assessment of model errors. The current ML functions rely on cross-validation. Theymore » utilize phase-error estimates that are calculated from a small fraction of diffraction data, called the test set, that are not used to fit the model. An approach has been developed that uses the work set to calculate the phase-error estimates in the ML refinement from simulating the model errors via the random displacement of atomic coordinates. It is called ML free-kick refinement as it uses the ML formulation of the target function and is based on the idea of freeing the model from the model bias imposed by the chemical energy restraints used in refinement. This approach for the calculation of error estimates is superior to the cross-validation approach: it reduces the phase error and increases the accuracy of molecular models, is more robust, provides clearer maps and may use a smaller portion of data for the test set for the calculation of R{sub free} or may leave it out completely.« less

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation.

PubMed

Wang, Fei; Li, Hong; Lu, Mingquan

2017-06-30

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks.

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation

PubMed Central

Li, Hong; Lu, Mingquan

2017-01-01

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks. PMID:28665318

Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model with Fixed Covariates

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan

2005-01-01

In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…

A general methodology for maximum likelihood inference from band-recovery data

USGS Publications Warehouse

Conroy, M.J.; Williams, B.K.

1984-01-01

A numerical procedure is described for obtaining maximum likelihood estimates and associated maximum likelihood inference from band- recovery data. The method is used to illustrate previously developed one-age-class band-recovery models, and is extended to new models, including the analysis with a covariate for survival rates and variable-time-period recovery models. Extensions to R-age-class band- recovery, mark-recapture models, and twice-yearly marking are discussed. A FORTRAN program provides computations for these models.

Maximum Likelihood Estimation of Nonlinear Structural Equation Models.

ERIC Educational Resources Information Center

Lee, Sik-Yum; Zhu, Hong-Tu

2002-01-01

Developed an EM type algorithm for maximum likelihood estimation of a general nonlinear structural equation model in which the E-step is completed by a Metropolis-Hastings algorithm. Illustrated the methodology with results from a simulation study and two real examples using data from previous studies. (SLD)

Maximum likelihood estimation for life distributions with competing failure modes

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1979-01-01

Systems which are placed on test at time zero, function for a period and die at some random time were studied. Failure may be due to one of several causes or modes. The parameters of the life distribution may depend upon the levels of various stress variables the item is subject to. Maximum likelihood estimation methods are discussed. Specific methods are reported for the smallest extreme-value distributions of life. Monte-Carlo results indicate the methods to be promising. Under appropriate conditions, the location parameters are nearly unbiased, the scale parameter is slight biased, and the asymptotic covariances are rapidly approached.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Mixture Rasch Models with Joint Maximum Likelihood Estimation

ERIC Educational Resources Information Center

Willse, John T.

2011-01-01

This research provides a demonstration of the utility of mixture Rasch models. Specifically, a model capable of estimating a mixture partial credit model using joint maximum likelihood is presented. Like the partial credit model, the mixture partial credit model has the beneficial feature of being appropriate for analysis of assessment data…

Low-complexity approximations to maximum likelihood MPSK modulation classification

NASA Technical Reports Server (NTRS)

Hamkins, Jon

2004-01-01

We present a new approximation to the maximum likelihood classifier to discriminate between M-ary and M'-ary phase-shift-keying transmitted on an additive white Gaussian noise (AWGN) channel and received noncoherentl, partially coherently, or coherently.

ILP-based maximum likelihood genome scaffolding

PubMed Central

2014-01-01

Background Interest in de novo genome assembly has been renewed in the past decade due to rapid advances in high-throughput sequencing (HTS) technologies which generate relatively short reads resulting in highly fragmented assemblies consisting of contigs. Additional long-range linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Due to library preparation artifacts and erroneous mapping of reads originating from repeats, scaffolding remains a challenging problem. In this paper, we provide a scalable scaffolding algorithm (SILP2) employing a maximum likelihood model capturing read mapping uncertainty and/or non-uniformity of contig coverage which is solved using integer linear programming. A Non-Serial Dynamic Programming (NSDP) paradigm is applied to render our algorithm useful in the processing of larger mammalian genomes. To compare scaffolding tools, we employ novel quantitative metrics in addition to the extant metrics in the field. We have also expanded the set of experiments to include scaffolding of low-complexity metagenomic samples. Results SILP2 achieves better scalability throughg a more efficient NSDP algorithm than previous release of SILP. The results show that SILP2 compares favorably to previous methods OPERA and MIP in both scalability and accuracy for scaffolding single genomes of up to human size, and significantly outperforms them on scaffolding low-complexity metagenomic samples. Conclusions Equipped with NSDP, SILP2 is able to scaffold large mammalian genomes, resulting in the longest and most accurate scaffolds. The ILP formulation for the maximum likelihood model is shown to be flexible enough to handle metagenomic samples. PMID:25253180

A maximum likelihood approach to diffeomorphic speckle tracking for 3D strain estimation in echocardiography.

PubMed

Curiale, Ariel H; Vegas-Sánchez-Ferrero, Gonzalo; Bosch, Johan G; Aja-Fernández, Santiago

2015-08-01

The strain and strain-rate measures are commonly used for the analysis and assessment of regional myocardial function. In echocardiography (EC), the strain analysis became possible using Tissue Doppler Imaging (TDI). Unfortunately, this modality shows an important limitation: the angle between the myocardial movement and the ultrasound beam should be small to provide reliable measures. This constraint makes it difficult to provide strain measures of the entire myocardium. Alternative non-Doppler techniques such as Speckle Tracking (ST) can provide strain measures without angle constraints. However, the spatial resolution and the noisy appearance of speckle still make the strain estimation a challenging task in EC. Several maximum likelihood approaches have been proposed to statistically characterize the behavior of speckle, which results in a better performance of speckle tracking. However, those models do not consider common transformations to achieve the final B-mode image (e.g. interpolation). This paper proposes a new maximum likelihood approach for speckle tracking which effectively characterizes speckle of the final B-mode image. Its formulation provides a diffeomorphic scheme than can be efficiently optimized with a second-order method. The novelty of the method is threefold: First, the statistical characterization of speckle generalizes conventional speckle models (Rayleigh, Nakagami and Gamma) to a more versatile model for real data. Second, the formulation includes local correlation to increase the efficiency of frame-to-frame speckle tracking. Third, a probabilistic myocardial tissue characterization is used to automatically identify more reliable myocardial motions. The accuracy and agreement assessment was evaluated on a set of 16 synthetic image sequences for three different scenarios: normal, acute ischemia and acute dyssynchrony. The proposed method was compared to six speckle tracking methods. Results revealed that the proposed method is the most

Maximum likelihood decoding analysis of accumulate-repeat-accumulate codes

NASA Technical Reports Server (NTRS)

Abbasfar, A.; Divsalar, D.; Yao, K.

2004-01-01

In this paper, the performance of the repeat-accumulate codes with (ML) decoding are analyzed and compared to random codes by very tight bounds. Some simple codes are shown that perform very close to Shannon limit with maximum likelihood decoding.

Cramer-Rao Bound, MUSIC, and Maximum Likelihood. Effects of Temporal Phase Difference

DTIC Science & Technology

1990-11-01

Technical Report 1373 November 1990 Cramer-Rao Bound, MUSIC , And Maximum Likelihood Effects of Temporal Phase o Difference C. V. TranI OTIC Approved... MUSIC , and Maximum Likelihood (ML) asymptotic variances corresponding to the two-source direction-of-arrival estimation where sources were modeled as...1pI = 1.00, SNR = 20 dB ..................................... 27 2. MUSIC for two equipowered signals impinging on a 5-element ULA (a) IpI = 0.50, SNR

Multimodal Likelihoods in Educational Assessment: Will the Real Maximum Likelihood Score Please Stand up?

ERIC Educational Resources Information Center

Wothke, Werner; Burket, George; Chen, Li-Sue; Gao, Furong; Shu, Lianghua; Chia, Mike

2011-01-01

It has been known for some time that item response theory (IRT) models may exhibit a likelihood function of a respondent's ability which may have multiple modes, flat modes, or both. These conditions, often associated with guessing of multiple-choice (MC) questions, can introduce uncertainty and bias to ability estimation by maximum likelihood…

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

NASA Technical Reports Server (NTRS)

Hoffbeck, Joseph P.; Landgrebe, David A.

1994-01-01

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

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

PubMed

Wu, Xiaowei; Zhu, Hongxiao

2015-02-07

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

Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models with Factor Structures

ERIC Educational Resources Information Center

Jeon, Minjeong; Rabe-Hesketh, Sophia

2012-01-01

In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be…

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Measuring Transcription Factor–Binding Site Turnover: A Maximum Likelihood Approach Using Phylogenies

PubMed Central

Otto, Wolfgang; Stadler, Peter F.; López-Giraldéz, Francesc; Townsend, Jeffrey P.; Lynch, Vincent J.

2009-01-01

A major mode of gene expression evolution is based on changes in cis-regulatory elements (CREs) whose function critically depends on the presence of transcription factor–binding sites (TFBS). Because CREs experience extensive TFBS turnover even with conserved function, alignment-based studies of CRE sequence evolution are limited to very closely related species. Here, we propose an alternative approach based on a stochastic model of TFBS turnover. We implemented a maximum likelihood model that permits variable turnover rates in different parts of the species tree. This model can be used to detect changes in turnover rate as a proxy for differences in the selective pressures acting on TFBS in different clades. We applied this method to five TFBS in the fungi methionine biosynthesis pathway and three TFBS in the HoxA clusters of vertebrates. We find that the estimated turnover rate is generally high, with half-life ranging between ∼5 and 150 My and a mode around tens of millions of years. This rate is consistent with the finding that even functionally conserved enhancers can show very low sequence similarity. We also detect statistically significant differences in the equilibrium densities of estrogen- and progesterone-response elements in the HoxA clusters between mammal and nonmammal vertebrates. Even more extreme clade-specific differences were found in the fungal data. We conclude that stochastic models of TFBS turnover enable the detection of shifts in the selective pressures acting on CREs in different organisms. The analysis tool, called CRETO (Cis-Regulatory Element Turn-Over) can be downloaded from http://www.bioinf.uni-leipzig.de/Software/creto/. PMID:20333180

A New Maximum Likelihood Approach for Free Energy Profile Construction from Molecular Simulations

PubMed Central

Lee, Tai-Sung; Radak, Brian K.; Pabis, Anna; York, Darrin M.

2013-01-01

A novel variational method for construction of free energy profiles from molecular simulation data is presented. The variational free energy profile (VFEP) method uses the maximum likelihood principle applied to the global free energy profile based on the entire set of simulation data (e.g from multiple biased simulations) that spans the free energy surface. The new method addresses common obstacles in two major problems usually observed in traditional methods for estimating free energy surfaces: the need for overlap in the re-weighting procedure and the problem of data representation. Test cases demonstrate that VFEP outperforms other methods in terms of the amount and sparsity of the data needed to construct the overall free energy profiles. For typical chemical reactions, only ~5 windows and ~20-35 independent data points per window are sufficient to obtain an overall qualitatively correct free energy profile with sampling errors an order of magnitude smaller than the free energy barrier. The proposed approach thus provides a feasible mechanism to quickly construct the global free energy profile and identify free energy barriers and basins in free energy simulations via a robust, variational procedure that determines an analytic representation of the free energy profile without the requirement of numerically unstable histograms or binning procedures. It can serve as a new framework for biased simulations and is suitable to be used together with other methods to tackle with the free energy estimation problem. PMID:23457427

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

On non-parametric maximum likelihood estimation of the bivariate survivor function.

PubMed

Prentice, R L

The likelihood function for the bivariate survivor function F, under independent censorship, is maximized to obtain a non-parametric maximum likelihood estimator &Fcirc;. &Fcirc; may or may not be unique depending on the configuration of singly- and doubly-censored pairs. The likelihood function can be maximized by placing all mass on the grid formed by the uncensored failure times, or half lines beyond the failure time grid, or in the upper right quadrant beyond the grid. By accumulating the mass along lines (or regions) where the likelihood is flat, one obtains a partially maximized likelihood as a function of parameters that can be uniquely estimated. The score equations corresponding to these point mass parameters are derived, using a Lagrange multiplier technique to ensure unit total mass, and a modified Newton procedure is used to calculate the parameter estimates in some limited simulation studies. Some considerations for the further development of non-parametric bivariate survivor function estimators are briefly described.

Estimating parameter of Rayleigh distribution by using Maximum Likelihood method and Bayes method

NASA Astrophysics Data System (ADS)

Ardianti, Fitri; Sutarman

2018-01-01

In this paper, we use Maximum Likelihood estimation and Bayes method under some risk function to estimate parameter of Rayleigh distribution to know the best method. The prior knowledge which used in Bayes method is Jeffrey’s non-informative prior. Maximum likelihood estimation and Bayes method under precautionary loss function, entropy loss function, loss function-L 1 will be compared. We compare these methods by bias and MSE value using R program. After that, the result will be displayed in tables to facilitate the comparisons.

On Muthen's Maximum Likelihood for Two-Level Covariance Structure Models

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro

2005-01-01

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures that take into account the dependence of such observations have been developed. Among procedures for 2-level covariance structure analysis, Muthen's maximum likelihood (MUML) has the advantage of easier computation and faster convergence. When…

THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures

PubMed Central

Theobald, Douglas L.; Wuttke, Deborah S.

2008-01-01

Summary THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. PMID:16777907

Concept for estimating mitochondrial DNA haplogroups using a maximum likelihood approach (EMMA)☆

PubMed Central

Röck, Alexander W.; Dür, Arne; van Oven, Mannis; Parson, Walther

2013-01-01

The assignment of haplogroups to mitochondrial DNA haplotypes contributes substantial value for quality control, not only in forensic genetics but also in population and medical genetics. The availability of Phylotree, a widely accepted phylogenetic tree of human mitochondrial DNA lineages, led to the development of several (semi-)automated software solutions for haplogrouping. However, currently existing haplogrouping tools only make use of haplogroup-defining mutations, whereas private mutations (beyond the haplogroup level) can be additionally informative allowing for enhanced haplogroup assignment. This is especially relevant in the case of (partial) control region sequences, which are mainly used in forensics. The present study makes three major contributions toward a more reliable, semi-automated estimation of mitochondrial haplogroups. First, a quality-controlled database consisting of 14,990 full mtGenomes downloaded from GenBank was compiled. Together with Phylotree, these mtGenomes serve as a reference database for haplogroup estimates. Second, the concept of fluctuation rates, i.e. a maximum likelihood estimation of the stability of mutations based on 19,171 full control region haplotypes for which raw lane data is available, is presented. Finally, an algorithm for estimating the haplogroup of an mtDNA sequence based on the combined database of full mtGenomes and Phylotree, which also incorporates the empirically determined fluctuation rates, is brought forward. On the basis of examples from the literature and EMPOP, the algorithm is not only validated, but both the strength of this approach and its utility for quality control of mitochondrial haplotypes is also demonstrated. PMID:23948335

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Statistical Bias in Maximum Likelihood Estimators of Item Parameters.

DTIC Science & Technology

1982-04-01

34 a> E r’r~e r ,C Ie I# ne,..,.rVi rnd Id.,flfv b1 - bindk numb.r) I; ,t-i i-cd I ’ tiie bias in the maximum likelihood ,st i- i;, ’ t iIeiIrs in...NTC, IL 60088 Psychometric Laboratory University of North Carolina I ERIC Facility-Acquisitions Davie Hall 013A 4833 Rugby Avenue Chapel Hill, NC

Maximum-likelihood soft-decision decoding of block codes using the A* algorithm

NASA Technical Reports Server (NTRS)

Ekroot, L.; Dolinar, S.

1994-01-01

The A* algorithm finds the path in a finite depth binary tree that optimizes a function. Here, it is applied to maximum-likelihood soft-decision decoding of block codes where the function optimized over the codewords is the likelihood function of the received sequence given each codeword. The algorithm considers codewords one bit at a time, making use of the most reliable received symbols first and pursuing only the partially expanded codewords that might be maximally likely. A version of the A* algorithm for maximum-likelihood decoding of block codes has been implemented for block codes up to 64 bits in length. The efficiency of this algorithm makes simulations of codes up to length 64 feasible. This article details the implementation currently in use, compares the decoding complexity with that of exhaustive search and Viterbi decoding algorithms, and presents performance curves obtained with this implementation of the A* algorithm for several codes.

Parallel implementation of D-Phylo algorithm for maximum likelihood clusters.

PubMed

Malik, Shamita; Sharma, Dolly; Khatri, Sunil Kumar

2017-03-01

This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k -means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8  ×  800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.

Recovery of Graded Response Model Parameters: A Comparison of Marginal Maximum Likelihood and Markov Chain Monte Carlo Estimation

ERIC Educational Resources Information Center

Kieftenbeld, Vincent; Natesan, Prathiba

2012-01-01

Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

Maximum likelihood estimates, from censored data, for mixed-Weibull distributions

NASA Astrophysics Data System (ADS)

Jiang, Siyuan; Kececioglu, Dimitri

1992-06-01

A new algorithm for estimating the parameters of mixed-Weibull distributions from censored data is presented. The algorithm follows the principle of maximum likelihood estimate (MLE) through the expectation and maximization (EM) algorithm, and it is derived for both postmortem and nonpostmortem time-to-failure data. It is concluded that the concept of the EM algorithm is easy to understand and apply (only elementary statistics and calculus are required). The log-likelihood function cannot decrease after an EM sequence; this important feature was observed in all of the numerical calculations. The MLEs of the nonpostmortem data were obtained successfully for mixed-Weibull distributions with up to 14 parameters in a 5-subpopulation, mixed-Weibull distribution. Numerical examples indicate that some of the log-likelihood functions of the mixed-Weibull distributions have multiple local maxima; therefore, the algorithm should start at several initial guesses of the parameter set.

Some Small Sample Results for Maximum Likelihood Estimation in Multidimensional Scaling.

ERIC Educational Resources Information Center

Ramsay, J. O.

1980-01-01

Some aspects of the small sample behavior of maximum likelihood estimates in multidimensional scaling are investigated with Monte Carlo techniques. In particular, the chi square test for dimensionality is examined and a correction for bias is proposed and evaluated. (Author/JKS)

12-mode OFDM transmission using reduced-complexity maximum likelihood detection.

PubMed

Lobato, Adriana; Chen, Yingkan; Jung, Yongmin; Chen, Haoshuo; Inan, Beril; Kuschnerov, Maxim; Fontaine, Nicolas K; Ryf, Roland; Spinnler, Bernhard; Lankl, Berthold

2015-02-01

We report the transmission of 163-Gb/s MDM-QPSK-OFDM and 245-Gb/s MDM-8QAM-OFDM transmission over 74 km of few-mode fiber supporting 12 spatial and polarization modes. A low-complexity maximum likelihood detector is employed to enhance the performance of a system impaired by mode-dependent loss.

L.U.St: a tool for approximated maximum likelihood supertree reconstruction.

PubMed

Akanni, Wasiu A; Creevey, Christopher J; Wilkinson, Mark; Pisani, Davide

2014-06-12

Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets. Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests). This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed. bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git. Davide.Pisani@bristol.ac.uk.

Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties

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

Stoneking, M.R.; Den Hartog, D.J.

1996-06-01

The fitting of data by {chi}{sup 2}-minimization is valid only when the uncertainties in the data are normally distributed. When analyzing spectroscopic or particle counting data at very low signal level (e.g., a Thomson scattering diagnostic), the uncertainties are distributed with a Poisson distribution. The authors have developed a maximum-likelihood method for fitting data that correctly treats the Poisson statistical character of the uncertainties. This method maximizes the total probability that the observed data are drawn from the assumed fit function using the Poisson probability function to determine the probability for each data point. The algorithm also returns uncertainty estimatesmore » for the fit parameters. They compare this method with a {chi}{sup 2}-minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than {approximately}20 counts per measurement). the maximum-likelihood method is found to be more accurate and robust, returning a narrower distribution of values for the fit parameters with fewer outliers.« less

Maximum likelihood decoding of Reed Solomon Codes

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

Sudan, M.

We present a randomized algorithm which takes as input n distinct points ((x{sub i}, y{sub i})){sup n}{sub i=1} from F x F (where F is a field) and integer parameters t and d and returns a list of all univariate polynomials f over F in the variable x of degree at most d which agree with the given set of points in at least t places (i.e., y{sub i} = f (x{sub i}) for at least t values of i), provided t = {Omega}({radical}nd). The running time is bounded by a polynomial in n. This immediately provides a maximum likelihoodmore » decoding algorithm for Reed Solomon Codes, which works in a setting with a larger number of errors than any previously known algorithm. To the best of our knowledge, this is the first efficient (i.e., polynomial time bounded) algorithm which provides some maximum likelihood decoding for any efficient (i.e., constant or even polynomial rate) code.« less

Approximate maximum likelihood decoding of block codes

NASA Technical Reports Server (NTRS)

Greenberger, H. J.

1979-01-01

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

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1975-01-01

A general iterative procedure is given for determining the consistent maximum likelihood estimates of normal distributions. In addition, a local maximum of the log-likelihood function, Newtons's method, a method of scoring, and modifications of these procedures are discussed.

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

PubMed

Guindon, Stéphane; Dufayard, Jean-François; Lefort, Vincent; Anisimova, Maria; Hordijk, Wim; Gascuel, Olivier

2010-05-01

PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.

Maximum likelihood techniques applied to quasi-elastic light scattering

NASA Technical Reports Server (NTRS)

Edwards, Robert V.

1992-01-01

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

A 3D approximate maximum likelihood localization solver

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

2016-09-23

A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with acoustic transmitters and vocalizing marine mammals to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives and support Marine Renewable Energy. An approximate maximum likelihood solver was developed using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.

THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.

PubMed

Theobald, Douglas L; Wuttke, Deborah S

2006-09-01

THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

NASA Astrophysics Data System (ADS)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

Vector Antenna and Maximum Likelihood Imaging for Radio Astronomy

DTIC Science & Technology

2016-03-05

Maximum Likelihood Imaging for Radio Astronomy Mary Knapp1, Frank Robey2, Ryan Volz3, Frank Lind3, Alan Fenn2, Alex Morris2, Mark Silver2, Sarah Klein2...haystack.mit.edu Abstract1— Radio astronomy using frequencies less than ~100 MHz provides a window into non-thermal processes in objects ranging from planets...observational astronomy . Ground-based observatories including LOFAR [1], LWA [2], [3], MWA [4], and the proposed SKA-Low [5], [6] are improving access to

An evaluation of percentile and maximum likelihood estimators of weibull paremeters

Treesearch

Stanley J. Zarnoch; Tommy R. Dell

1985-01-01

Two methods of estimating the three-parameter Weibull distribution were evaluated by computer simulation and field data comparison. Maximum likelihood estimators (MLB) with bias correction were calculated with the computer routine FITTER (Bailey 1974); percentile estimators (PCT) were those proposed by Zanakis (1979). The MLB estimators had superior smaller bias and...

Maximum likelihood conjoint measurement of lightness and chroma.

PubMed

Rogers, Marie; Knoblauch, Kenneth; Franklin, Anna

2016-03-01

Color varies along dimensions of lightness, hue, and chroma. We used maximum likelihood conjoint measurement to investigate how lightness and chroma influence color judgments. Observers judged lightness and chroma of stimuli that varied in both dimensions in a paired-comparison task. We modeled how changes in one dimension influenced judgment of the other. An additive model best fit the data in all conditions except for judgment of red chroma where there was a small but significant interaction. Lightness negatively contributed to perception of chroma for red, blue, and green hues but not for yellow. The method permits quantification of lightness and chroma contributions to color appearance.

The Extended-Image Tracking Technique Based on the Maximum Likelihood Estimation

NASA Technical Reports Server (NTRS)

Tsou, Haiping; Yan, Tsun-Yee

2000-01-01

This paper describes an extended-image tracking technique based on the maximum likelihood estimation. The target image is assume to have a known profile covering more than one element of a focal plane detector array. It is assumed that the relative position between the imager and the target is changing with time and the received target image has each of its pixels disturbed by an independent additive white Gaussian noise. When a rotation-invariant movement between imager and target is considered, the maximum likelihood based image tracking technique described in this paper is a closed-loop structure capable of providing iterative update of the movement estimate by calculating the loop feedback signals from a weighted correlation between the currently received target image and the previously estimated reference image in the transform domain. The movement estimate is then used to direct the imager to closely follow the moving target. This image tracking technique has many potential applications, including free-space optical communications and astronomy where accurate and stabilized optical pointing is essential.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Correcting for sequencing error in maximum likelihood phylogeny inference.

PubMed

Kuhner, Mary K; McGill, James

2014-11-04

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

Maximum likelihood convolutional decoding (MCD) performance due to system losses

NASA Technical Reports Server (NTRS)

Webster, L.

1976-01-01

A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1978-01-01

This paper addresses the problem of obtaining numerically maximum-likelihood estimates of the parameters for a mixture of normal distributions. In recent literature, a certain successive-approximations procedure, based on the likelihood equations, was shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, we introduce a general iterative procedure, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. We show that, with probability 1 as the sample size grows large, this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. We also show that the step-size which yields optimal local convergence rates for large samples is determined in a sense by the 'separation' of the component normal densities and is bounded below by a number between 1 and 2.

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Lee, John C. K.

2003-01-01

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models…

An EM Algorithm for Maximum Likelihood Estimation of Process Factor Analysis Models

ERIC Educational Resources Information Center

Lee, Taehun

2010-01-01

In this dissertation, an Expectation-Maximization (EM) algorithm is developed and implemented to obtain maximum likelihood estimates of the parameters and the associated standard error estimates characterizing temporal flows for the latent variable time series following stationary vector ARMA processes, as well as the parameters defining the…

The numerical evaluation of maximum-likelihood estimates of the parameters for a mixture of normal distributions from partially identified samples

NASA Technical Reports Server (NTRS)

Walker, H. F.

1976-01-01

Likelihood equations determined by the two types of samples which are necessary conditions for a maximum-likelihood estimate were considered. These equations suggest certain successive approximations iterative procedures for obtaining maximum likelihood estimates. The procedures, which are generalized steepest ascent (deflected gradient) procedures, contain those of Hosmer as a special case.

Maximum Likelihood Item Easiness Models for Test Theory Without an Answer Key

PubMed Central

Batchelder, William H.

2014-01-01

Cultural consensus theory (CCT) is a data aggregation technique with many applications in the social and behavioral sciences. We describe the intuition and theory behind a set of CCT models for continuous type data using maximum likelihood inference methodology. We describe how bias parameters can be incorporated into these models. We introduce two extensions to the basic model in order to account for item rating easiness/difficulty. The first extension is a multiplicative model and the second is an additive model. We show how the multiplicative model is related to the Rasch model. We describe several maximum-likelihood estimation procedures for the models and discuss issues of model fit and identifiability. We describe how the CCT models could be used to give alternative consensus-based measures of reliability. We demonstrate the utility of both the basic and extended models on a set of essay rating data and give ideas for future research. PMID:29795812

SCI Identification (SCIDNT) program user's guide. [maximum likelihood method for linear rotorcraft models

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.

Maximum likelihood-based analysis of single-molecule photon arrival trajectories

NASA Astrophysics Data System (ADS)

Hajdziona, Marta; Molski, Andrzej

2011-02-01

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 103 photons. When the intensity levels are well-separated and 104 photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

Maximum likelihood-based analysis of single-molecule photon arrival trajectories.

PubMed

Hajdziona, Marta; Molski, Andrzej

2011-02-07

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 10(3) photons. When the intensity levels are well-separated and 10(4) photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

PubMed

Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

Bias Correction for the Maximum Likelihood Estimate of Ability. Research Report. ETS RR-05-15

ERIC Educational Resources Information Center

Zhang, Jinming

2005-01-01

Lord's bias function and the weighted likelihood estimation method are effective in reducing the bias of the maximum likelihood estimate of an examinee's ability under the assumption that the true item parameters are known. This paper presents simulation studies to determine the effectiveness of these two methods in reducing the bias when the item…

Simple Penalties on Maximum-Likelihood Estimates of Genetic Parameters to Reduce Sampling Variation

PubMed Central

Meyer, Karin

2016-01-01

Multivariate estimates of genetic parameters are subject to substantial sampling variation, especially for smaller data sets and more than a few traits. A simple modification of standard, maximum-likelihood procedures for multivariate analyses to estimate genetic covariances is described, which can improve estimates by substantially reducing their sampling variances. This is achieved by maximizing the likelihood subject to a penalty. Borrowing from Bayesian principles, we propose a mild, default penalty—derived assuming a Beta distribution of scale-free functions of the covariance components to be estimated—rather than laboriously attempting to determine the stringency of penalization from the data. An extensive simulation study is presented, demonstrating that such penalties can yield very worthwhile reductions in loss, i.e., the difference from population values, for a wide range of scenarios and without distorting estimates of phenotypic covariances. Moreover, mild default penalties tend not to increase loss in difficult cases and, on average, achieve reductions in loss of similar magnitude to computationally demanding schemes to optimize the degree of penalization. Pertinent details required for the adaptation of standard algorithms to locate the maximum of the likelihood function are outlined. PMID:27317681

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood versus Robust Methods

ERIC Educational Resources Information Center

Zhong, Xiaoling; Yuan, Ke-Hai

2011-01-01

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data,…

Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

PubMed

Pritikin, Joshua N; Brick, Timothy R; Neale, Michael C

2018-04-01

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

Constrained Maximum Likelihood Estimation for Two-Level Mean and Covariance Structure Models

ERIC Educational Resources Information Center

Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai

2011-01-01

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in…

Maximum Likelihood Item Easiness Models for Test Theory without an Answer Key

ERIC Educational Resources Information Center

France, Stephen L.; Batchelder, William H.

2015-01-01

Cultural consensus theory (CCT) is a data aggregation technique with many applications in the social and behavioral sciences. We describe the intuition and theory behind a set of CCT models for continuous type data using maximum likelihood inference methodology. We describe how bias parameters can be incorporated into these models. We introduce…

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions, Addendum

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1975-01-01

New results and insights concerning a previously published iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions were discussed. It was shown that the procedure converges locally to the consistent maximum likelihood estimate as long as a specified parameter is bounded between two limits. Bound values were given to yield optimal local convergence.

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm. [for affine transformation of crop inventory data

NASA Technical Reports Server (NTRS)

Thadani, S. G.

1977-01-01

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

The effect of prenatal care on birthweight: a full-information maximum likelihood approach.

PubMed

Rous, Jeffrey J; Jewell, R Todd; Brown, Robert W

2004-03-01

This paper uses a full-information maximum likelihood estimation procedure, the Discrete Factor Method, to estimate the relationship between birthweight and prenatal care. This technique controls for the potential biases surrounding both the sample selection of the pregnancy-resolution decision and the endogeneity of prenatal care. In addition, we use the actual number of prenatal care visits; other studies have normally measured prenatal care as the month care is initiated. We estimate a birthweight production function using 1993 data from the US state of Texas. The results underscore the importance of correcting for estimation problems. Specifically, a model that does not control for sample selection and endogeneity overestimates the benefit of an additional visit for women who have relatively few visits. This overestimation may indicate 'positive fetal selection,' i.e., women who did not abort may have healthier babies. Also, a model that does not control for self-selection and endogenity predicts that past 17 visits, an additional visit leads to lower birthweight, while a model that corrects for these estimation problems predicts a positive effect for additional visits. This result shows the effect of mothers with less healthy fetuses making more prenatal care visits, known as 'adverse selection' in prenatal care. Copyright 2003 John Wiley & Sons, Ltd.

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions, 2

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1976-01-01

The problem of obtaining numerically maximum likelihood estimates of the parameters for a mixture of normal distributions is addressed. In recent literature, a certain successive approximations procedure, based on the likelihood equations, is shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, a general iterative procedure is introduced, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. With probability 1 as the sample size grows large, it is shown that this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. The step-size which yields optimal local convergence rates for large samples is determined in a sense by the separation of the component normal densities and is bounded below by a number between 1 and 2.

A maximum likelihood convolutional decoder model vs experimental data comparison

NASA Technical Reports Server (NTRS)

Chen, R. Y.

1979-01-01

This article describes the comparison of a maximum likelihood convolutional decoder (MCD) prediction model and the actual performance of the MCD at the Madrid Deep Space Station. The MCD prediction model is used to develop a subroutine that has been utilized by the Telemetry Analysis Program (TAP) to compute the MCD bit error rate for a given signal-to-noise ratio. The results indicate that that the TAP can predict quite well compared to the experimental measurements. An optimal modulation index also can be found through TAP.

Maximum likelihood resampling of noisy, spatially correlated data

NASA Astrophysics Data System (ADS)

Goff, J.; Jenkins, C.

2005-12-01

In any geologic application, noisy data are sources of consternation for researchers, inhibiting interpretability and marring images with unsightly and unrealistic artifacts. Filtering is the typical solution to dealing with noisy data. However, filtering commonly suffers from ad hoc (i.e., uncalibrated, ungoverned) application, which runs the risk of erasing high variability components of the field in addition to the noise components. We present here an alternative to filtering: a newly developed methodology for correcting noise in data by finding the "best" value given the data value, its uncertainty, and the data values and uncertainties at proximal locations. The motivating rationale is that data points that are close to each other in space cannot differ by "too much", where how much is "too much" is governed by the field correlation properties. Data with large uncertainties will frequently violate this condition, and in such cases need to be corrected, or "resampled." The best solution for resampling is determined by the maximum of the likelihood function defined by the intersection of two probability density functions (pdf): (1) the data pdf, with mean and variance determined by the data value and square uncertainty, respectively, and (2) the geostatistical pdf, whose mean and variance are determined by the kriging algorithm applied to proximal data values. A Monte Carlo sampling of the data probability space eliminates non-uniqueness, and weights the solution toward data values with lower uncertainties. A test with a synthetic data set sampled from a known field demonstrates quantitatively and qualitatively the improvement provided by the maximum likelihood resampling algorithm. The method is also applied to three marine geology/geophysics data examples: (1) three generations of bathymetric data on the New Jersey shelf with disparate data uncertainties; (2) mean grain size data from the Adriatic Sea, which is combination of both analytic (low uncertainty

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

NASA Technical Reports Server (NTRS)

Klein, V.

1980-01-01

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

Varied applications of a new maximum-likelihood code with complete covariance capability. [FERRET, for data adjustment

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

Schmittroth, F.

1978-01-01

Applications of a new data-adjustment code are given. The method is based on a maximum-likelihood extension of generalized least-squares methods that allow complete covariance descriptions for the input data and the final adjusted data evaluations. The maximum-likelihood approach is used with a generalized log-normal distribution that provides a way to treat problems with large uncertainties and that circumvents the problem of negative values that can occur for physically positive quantities. The computer code, FERRET, is written to enable the user to apply it to a large variety of problems by modifying only the input subroutine. The following applications are discussed:more » A 75-group a priori damage function is adjusted by as much as a factor of two by use of 14 integral measurements in different reactor spectra. Reactor spectra and dosimeter cross sections are simultaneously adjusted on the basis of both integral measurements and experimental proton-recoil spectra. The simultaneous use of measured reaction rates, measured worths, microscopic measurements, and theoretical models are used to evaluate dosimeter and fission-product cross sections. Applications in the data reduction of neutron cross section measurements and in the evaluation of reactor after-heat are also considered. 6 figures.« less

Variance Difference between Maximum Likelihood Estimation Method and Expected A Posteriori Estimation Method Viewed from Number of Test Items

ERIC Educational Resources Information Center

Mahmud, Jumailiyah; Sutikno, Muzayanah; Naga, Dali S.

2016-01-01

The aim of this study is to determine variance difference between maximum likelihood and expected A posteriori estimation methods viewed from number of test items of aptitude test. The variance presents an accuracy generated by both maximum likelihood and Bayes estimation methods. The test consists of three subtests, each with 40 multiple-choice…

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations.

PubMed

Can, Seda; van de Schoot, Rens; Hox, Joop

2015-06-01

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

Maximum Likelihood Shift Estimation Using High Resolution Polarimetric SAR Clutter Model

NASA Astrophysics Data System (ADS)

Harant, Olivier; Bombrun, Lionel; Vasile, Gabriel; Ferro-Famil, Laurent; Gay, Michel

2011-03-01

This paper deals with a Maximum Likelihood (ML) shift estimation method in the context of High Resolution (HR) Polarimetric SAR (PolSAR) clutter. Texture modeling is exposed and the generalized ML texture tracking method is extended to the merging of various sensors. Some results on displacement estimation on the Argentiere glacier in the Mont Blanc massif using dual-pol TerraSAR-X (TSX) and quad-pol RADARSAT-2 (RS2) sensors are finally discussed.

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models

ERIC Educational Resources Information Center

Casabianca, Jodi M.; Lewis, Charles

2015-01-01

Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…

A Study of Item Bias for Attitudinal Measurement Using Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Mayberry, Paul W.

A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…

The Effects of Model Misspecification and Sample Size on LISREL Maximum Likelihood Estimates.

ERIC Educational Resources Information Center

Baldwin, Beatrice

The robustness of LISREL computer program maximum likelihood estimates under specific conditions of model misspecification and sample size was examined. The population model used in this study contains one exogenous variable; three endogenous variables; and eight indicator variables, two for each latent variable. Conditions of model…

An Iterative Maximum a Posteriori Estimation of Proficiency Level to Detect Multiple Local Likelihood Maxima

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles

2010-01-01

In this article the authors focus on the issue of the nonuniqueness of the maximum likelihood (ML) estimator of proficiency level in item response theory (with special attention to logistic models). The usual maximum a posteriori (MAP) method offers a good alternative within that framework; however, this article highlights some drawbacks of its…

A low-power, high-throughput maximum-likelihood convolutional decoder chip for NASA's 30/20 GHz program

NASA Technical Reports Server (NTRS)

Mccallister, R. D.; Crawford, J. J.

1981-01-01

It is pointed out that the NASA 30/20 GHz program will place in geosynchronous orbit a technically advanced communication satellite which can process time-division multiple access (TDMA) information bursts with a data throughput in excess of 4 GBPS. To guarantee acceptable data quality during periods of signal attenuation it will be necessary to provide a significant forward error correction (FEC) capability. Convolutional decoding (utilizing the maximum-likelihood techniques) was identified as the most attractive FEC strategy. Design trade-offs regarding a maximum-likelihood convolutional decoder (MCD) in a single-chip CMOS implementation are discussed.

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

Bias correction for estimated QTL effects using the penalized maximum likelihood method.

PubMed

Zhang, J; Yue, C; Zhang, Y-M

2012-04-01

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

Parameter estimation of history-dependent leaky integrate-and-fire neurons using maximum-likelihood methods

PubMed Central

Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

2012-01-01

When a neuronal spike train is observed, what can we say about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then to choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate and fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that its unique global minimum can thus be found by gradient descent techniques. The global minimum property requires independence of spike time intervals. Lack of history dependence is, however, an important constraint that is not fulfilled in many biological neurons which are known to generate a rich repertoire of spiking behaviors that are incompatible with history independence. Therefore, we expanded the integrate and fire model by including one additional variable, a variable threshold (Mihalas & Niebur, 2009) allowing for history-dependent firing patterns. This neuronal model produces a large number of spiking behaviors while still being linear. Linearity is important as it maintains the distribution of the random variables and still allows for maximum likelihood methods to be used. In this study we show that, although convexity of the negative log-likelihood is not guaranteed for this model, the minimum of the negative log-likelihood function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) frequently reaches the global minimum. PMID:21851282

Maximum likelihood pedigree reconstruction using integer linear programming.

PubMed

Cussens, James; Bartlett, Mark; Jones, Elinor M; Sheehan, Nuala A

2013-01-01

Large population biobanks of unrelated individuals have been highly successful in detecting common genetic variants affecting diseases of public health concern. However, they lack the statistical power to detect more modest gene-gene and gene-environment interaction effects or the effects of rare variants for which related individuals are ideally required. In reality, most large population studies will undoubtedly contain sets of undeclared relatives, or pedigrees. Although a crude measure of relatedness might sometimes suffice, having a good estimate of the true pedigree would be much more informative if this could be obtained efficiently. Relatives are more likely to share longer haplotypes around disease susceptibility loci and are hence biologically more informative for rare variants than unrelated cases and controls. Distant relatives are arguably more useful for detecting variants with small effects because they are less likely to share masking environmental effects. Moreover, the identification of relatives enables appropriate adjustments of statistical analyses that typically assume unrelatedness. We propose to exploit an integer linear programming optimisation approach to pedigree learning, which is adapted to find valid pedigrees by imposing appropriate constraints. Our method is not restricted to small pedigrees and is guaranteed to return a maximum likelihood pedigree. With additional constraints, we can also search for multiple high-probability pedigrees and thus account for the inherent uncertainty in any particular pedigree reconstruction. The true pedigree is found very quickly by comparison with other methods when all individuals are observed. Extensions to more complex problems seem feasible. © 2012 Wiley Periodicals, Inc.

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis.

PubMed

Falk, Carl F; Cai, Li

2016-06-01

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.

Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown

ERIC Educational Resources Information Center

Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi

2014-01-01

When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…

On the quirks of maximum parsimony and likelihood on phylogenetic networks.

PubMed

Bryant, Christopher; Fischer, Mareike; Linz, Simone; Semple, Charles

2017-03-21

Maximum parsimony is one of the most frequently-discussed tree reconstruction methods in phylogenetic estimation. However, in recent years it has become more and more apparent that phylogenetic trees are often not sufficient to describe evolution accurately. For instance, processes like hybridization or lateral gene transfer that are commonplace in many groups of organisms and result in mosaic patterns of relationships cannot be represented by a single phylogenetic tree. This is why phylogenetic networks, which can display such events, are becoming of more and more interest in phylogenetic research. It is therefore necessary to extend concepts like maximum parsimony from phylogenetic trees to networks. Several suggestions for possible extensions can be found in recent literature, for instance the softwired and the hardwired parsimony concepts. In this paper, we analyze the so-called big parsimony problem under these two concepts, i.e. we investigate maximum parsimonious networks and analyze their properties. In particular, we show that finding a softwired maximum parsimony network is possible in polynomial time. We also show that the set of maximum parsimony networks for the hardwired definition always contains at least one phylogenetic tree. Lastly, we investigate some parallels of parsimony to different likelihood concepts on phylogenetic networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new maximum-likelihood change estimator for two-pass SAR coherent change detection

DOE PAGES

Wahl, Daniel E.; Yocky, David A.; Jakowatz, Jr., Charles V.; ...

2016-01-11

In previous research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimatormore » is a surprisingly simple expression, easy to implement, and optimal in the ML sense. As a result, this new estimate produces improved results in the coherent pair collects that we have tested.« less

Case-Deletion Diagnostics for Maximum Likelihood Multipoint Quantitative Trait Locus Linkage Analysis

PubMed Central

Mendoza, Maria C.B.; Burns, Trudy L.; Jones, Michael P.

2009-01-01

Objectives Case-deletion diagnostic methods are tools that allow identification of influential observations that may affect parameter estimates and model fitting conclusions. The goal of this paper was to develop two case-deletion diagnostics, the exact case deletion (ECD) and the empirical influence function (EIF), for detecting outliers that can affect results of sib-pair maximum likelihood quantitative trait locus (QTL) linkage analysis. Methods Subroutines to compute the ECD and EIF were incorporated into the maximum likelihood QTL variance estimation components of the linkage analysis program MAPMAKER/SIBS. Performance of the diagnostics was compared in simulation studies that evaluated the proportion of outliers correctly identified (sensitivity), and the proportion of non-outliers correctly identified (specificity). Results Simulations involving nuclear family data sets with one outlier showed EIF sensitivities approximated ECD sensitivities well for outlier-affected parameters. Sensitivities were high, indicating the outlier was identified a high proportion of the time. Simulations also showed the enormous computational time advantage of the EIF. Diagnostics applied to body mass index in nuclear families detected observations influential on the lod score and model parameter estimates. Conclusions The EIF is a practical diagnostic tool that has the advantages of high sensitivity and quick computation. PMID:19172086

DSN telemetry system performance using a maximum likelihood convolutional decoder

NASA Technical Reports Server (NTRS)

Benjauthrit, B.; Kemp, R. P.

1977-01-01

Results are described of telemetry system performance testing using DSN equipment and a Maximum Likelihood Convolutional Decoder (MCD) for code rates 1/2 and 1/3, constraint length 7 and special test software. The test results confirm the superiority of the rate 1/3 over that of the rate 1/2. The overall system performance losses determined at the output of the Symbol Synchronizer Assembly are less than 0.5 db for both code rates. Comparison of the performance is also made with existing mathematical models. Error statistics of the decoded data are examined. The MCD operational threshold is found to be about 1.96 db.

A maximum likelihood algorithm for genome mapping of cytogenetic loci from meiotic configuration data.

PubMed Central

Reyes-Valdés, M H; Stelly, D M

1995-01-01

Frequencies of meiotic configurations in cytogenetic stocks are dependent on chiasma frequencies in segments defined by centromeres, breakpoints, and telomeres. The expectation maximization algorithm is proposed as a general method to perform maximum likelihood estimations of the chiasma frequencies in the intervals between such locations. The estimates can be translated via mapping functions into genetic maps of cytogenetic landmarks. One set of observational data was analyzed to exemplify application of these methods, results of which were largely concordant with other comparable data. The method was also tested by Monte Carlo simulation of frequencies of meiotic configurations from a monotelodisomic translocation heterozygote, assuming six different sample sizes. The estimate averages were always close to the values given initially to the parameters. The maximum likelihood estimation procedures can be extended readily to other kinds of cytogenetic stocks and allow the pooling of diverse cytogenetic data to collectively estimate lengths of segments, arms, and chromosomes. Images Fig. 1 PMID:7568226

Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques.

PubMed

Schminkey, Donna L; von Oertzen, Timo; Bullock, Linda

2016-08-01

With increasing access to population-based data and electronic health records for secondary analysis, missing data are common. In the social and behavioral sciences, missing data frequently are handled with multiple imputation methods or full information maximum likelihood (FIML) techniques, but healthcare researchers have not embraced these methodologies to the same extent and more often use either traditional imputation techniques or complete case analysis, which can compromise power and introduce unintended bias. This article is a review of options for handling missing data, concluding with a case study demonstrating the utility of multilevel structural equation modeling using full information maximum likelihood (MSEM with FIML) to handle large amounts of missing data. MSEM with FIML is a parsimonious and hypothesis-driven strategy to cope with large amounts of missing data without compromising power or introducing bias. This technique is relevant for nurse researchers faced with ever-increasing amounts of electronic data and decreasing research budgets. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas

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

Washeleski, Robert L.; Meyer, Edmond J. IV; King, Lyon B.

2013-10-15

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. Themore » key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.« less

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas.

PubMed

Washeleski, Robert L; Meyer, Edmond J; King, Lyon B

2013-10-01

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. The key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.

Quasi-Maximum Likelihood Estimation of Structural Equation Models with Multiple Interaction and Quadratic Effects

ERIC Educational Resources Information Center

Klein, Andreas G.; Muthen, Bengt O.

2007-01-01

In this article, a nonlinear structural equation model is introduced and a quasi-maximum likelihood method for simultaneous estimation and testing of multiple nonlinear effects is developed. The focus of the new methodology lies on efficiency, robustness, and computational practicability. Monte-Carlo studies indicate that the method is highly…

Computing maximum-likelihood estimates for parameters of the National Descriptive Model of Mercury in Fish

USGS Publications Warehouse

Donato, David I.

2012-01-01

This report presents the mathematical expressions and the computational techniques required to compute maximum-likelihood estimates for the parameters of the National Descriptive Model of Mercury in Fish (NDMMF), a statistical model used to predict the concentration of methylmercury in fish tissue. The expressions and techniques reported here were prepared to support the development of custom software capable of computing NDMMF parameter estimates more quickly and using less computer memory than is currently possible with available general-purpose statistical software. Computation of maximum-likelihood estimates for the NDMMF by numerical solution of a system of simultaneous equations through repeated Newton-Raphson iterations is described. This report explains the derivation of the mathematical expressions required for computational parameter estimation in sufficient detail to facilitate future derivations for any revised versions of the NDMMF that may be developed.

Applications of non-standard maximum likelihood techniques in energy and resource economics

NASA Astrophysics Data System (ADS)

Moeltner, Klaus

Two important types of non-standard maximum likelihood techniques, Simulated Maximum Likelihood (SML) and Pseudo-Maximum Likelihood (PML), have only recently found consideration in the applied economic literature. The objective of this thesis is to demonstrate how these methods can be successfully employed in the analysis of energy and resource models. Chapter I focuses on SML. It constitutes the first application of this technique in the field of energy economics. The framework is as follows: Surveys on the cost of power outages to commercial and industrial customers usually capture multiple observations on the dependent variable for a given firm. The resulting pooled data set is censored and exhibits cross-sectional heterogeneity. We propose a model that addresses these issues by allowing regression coefficients to vary randomly across respondents and by using the Geweke-Hajivassiliou-Keane simulator and Halton sequences to estimate high-order cumulative distribution terms. This adjustment requires the use of SML in the estimation process. Our framework allows for a more comprehensive analysis of outage costs than existing models, which rely on the assumptions of parameter constancy and cross-sectional homogeneity. Our results strongly reject both of these restrictions. The central topic of the second Chapter is the use of PML, a robust estimation technique, in count data analysis of visitor demand for a system of recreation sites. PML has been popular with researchers in this context, since it guards against many types of mis-specification errors. We demonstrate, however, that estimation results will generally be biased even if derived through PML if the recreation model is based on aggregate, or zonal data. To countervail this problem, we propose a zonal model of recreation that captures some of the underlying heterogeneity of individual visitors by incorporating distributional information on per-capita income into the aggregate demand function. This adjustment

Joint maximum-likelihood magnitudes of presumed underground nuclear test explosions

NASA Astrophysics Data System (ADS)

Peacock, Sheila; Douglas, Alan; Bowers, David

2017-08-01

Body-wave magnitudes (mb) of 606 seismic disturbances caused by presumed underground nuclear test explosions at specific test sites between 1964 and 1996 have been derived from station amplitudes collected by the International Seismological Centre (ISC), by a joint inversion for mb and station-specific magnitude corrections. A maximum-likelihood method was used to reduce the upward bias of network mean magnitudes caused by data censoring, where arrivals at stations that do not report arrivals are assumed to be hidden by the ambient noise at the time. Threshold noise levels at each station were derived from the ISC amplitudes using the method of Kelly and Lacoss, which fits to the observed magnitude-frequency distribution a Gutenberg-Richter exponential decay truncated at low magnitudes by an error function representing the low-magnitude threshold of the station. The joint maximum-likelihood inversion is applied to arrivals from the sites: Semipalatinsk (Kazakhstan) and Novaya Zemlya, former Soviet Union; Singer (Lop Nor), China; Mururoa and Fangataufa, French Polynesia; and Nevada, USA. At sites where eight or more arrivals could be used to derive magnitudes and station terms for 25 or more explosions (Nevada, Semipalatinsk and Mururoa), the resulting magnitudes and station terms were fixed and a second inversion carried out to derive magnitudes for additional explosions with three or more arrivals. 93 more magnitudes were thus derived. During processing for station thresholds, many stations were rejected for sparsity of data, obvious errors in reported amplitude, or great departure of the reported amplitude-frequency distribution from the expected left-truncated exponential decay. Abrupt changes in monthly mean amplitude at a station apparently coincide with changes in recording equipment and/or analysis method at the station.

Maximum likelihood of phylogenetic networks.

PubMed

Jin, Guohua; Nakhleh, Luay; Snir, Sagi; Tuller, Tamir

2006-11-01

Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications for human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative. In this article we provide a new HGT-oriented likelihood framework for many problems that involve phylogeny-based HGT detection and reconstruction. Beside the formulation of various likelihood criteria, we show that most of these problems are NP-hard, and offer heuristics for efficient and accurate reconstruction of HGT under these criteria. We implemented our heuristics and used them to analyze biological as well as synthetic data. In both cases, our criteria and heuristics exhibited very good performance with respect to identifying the correct number of HGT events as well as inferring their correct location on the species tree. Implementation of the criteria as well as heuristics and hardness proofs are available from the authors upon request. Hardness proofs can also be downloaded at http://www.cs.tau.ac.il/~tamirtul/MLNET/Supp-ML.pdf

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

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

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

Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

NASA Astrophysics Data System (ADS)

Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

1994-06-01

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

Applying a Weighted Maximum Likelihood Latent Trait Estimator to the Generalized Partial Credit Model

ERIC Educational Resources Information Center

Penfield, Randall D.; Bergeron, Jennifer M.

2005-01-01

This article applies a weighted maximum likelihood (WML) latent trait estimator to the generalized partial credit model (GPCM). The relevant equations required to obtain the WML estimator using the Newton-Raphson algorithm are presented, and a simulation study is described that compared the properties of the WML estimator to those of the maximum…

Richardson-Lucy/maximum likelihood image restoration algorithm for fluorescence microscopy: further testing.

PubMed

Holmes, T J; Liu, Y H

1989-11-15

A maximum likelihood based iterative algorithm adapted from nuclear medicine imaging for noncoherent optical imaging was presented in a previous publication with some initial computer-simulation testing. This algorithm is identical in form to that previously derived in a different way by W. H. Richardson "Bayesian-Based Iterative Method of Image Restoration," J. Opt. Soc. Am. 62, 55-59 (1972) and L. B. Lucy "An Iterative Technique for the Rectification of Observed Distributions," Astron. J. 79, 745-765 (1974). Foreseen applications include superresolution and 3-D fluorescence microscopy. This paper presents further simulation testing of this algorithm and a preliminary experiment with a defocused camera. The simulations show quantified resolution improvement as a function of iteration number, and they show qualitatively the trend in limitations on restored resolution when noise is present in the data. Also shown are results of a simulation in restoring missing-cone information for 3-D imaging. Conclusions are in support of the feasibility of using these methods with real systems, while computational cost and timing estimates indicate that it should be realistic to implement these methods. Itis suggested in the Appendix that future extensions to the maximum likelihood based derivation of this algorithm will address some of the limitations that are experienced with the nonextended form of the algorithm presented here.

Maximum likelihood estimation of label imperfections and its use in the identification of mislabeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of estimating label imperfections and the use of the estimation in identifying mislabeled patterns is presented. Expressions for the maximum likelihood estimates of classification errors and a priori probabilities are derived from the classification of a set of labeled patterns. Expressions also are given for the asymptotic variances of probability of correct classification and proportions. Simple models are developed for imperfections in the labels and for classification errors and are used in the formulation of a maximum likelihood estimation scheme. Schemes are presented for the identification of mislabeled patterns in terms of threshold on the discriminant functions for both two-class and multiclass cases. Expressions are derived for the probability that the imperfect label identification scheme will result in a wrong decision and are used in computing thresholds. The results of practical applications of these techniques in the processing of remotely sensed multispectral data are presented.

Gyre and gimble: a maximum-likelihood replacement for Patterson correlation refinement.

PubMed

McCoy, Airlie J; Oeffner, Robert D; Millán, Claudia; Sammito, Massimo; Usón, Isabel; Read, Randy J

2018-04-01

Descriptions are given of the maximum-likelihood gyre method implemented in Phaser for optimizing the orientation and relative position of rigid-body fragments of a model after the orientation of the model has been identified, but before the model has been positioned in the unit cell, and also the related gimble method for the refinement of rigid-body fragments of the model after positioning. Gyre refinement helps to lower the root-mean-square atomic displacements between model and target molecular-replacement solutions for the test case of antibody Fab(26-10) and improves structure solution with ARCIMBOLDO_SHREDDER.

F-8C adaptive flight control extensions. [for maximum likelihood estimation

NASA Technical Reports Server (NTRS)

Stein, G.; Hartmann, G. L.

1977-01-01

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.

DSN telemetry system performance with convolutionally coded data using operational maximum-likelihood convolutional decoders

NASA Technical Reports Server (NTRS)

Benjauthrit, B.; Mulhall, B.; Madsen, B. D.; Alberda, M. E.

1976-01-01

The DSN telemetry system performance with convolutionally coded data using the operational maximum-likelihood convolutional decoder (MCD) being implemented in the Network is described. Data rates from 80 bps to 115.2 kbps and both S- and X-band receivers are reported. The results of both one- and two-way radio losses are included.

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.

PubMed

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-04-06

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

PubMed Central

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-01-01

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods. PMID:28383503

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement.

PubMed

Li, Haiyan; Wu, Jun; Miao, Aimin; Yu, Pengfei; Chen, Jianhua; Zhang, Yufeng

2017-04-17

Ultrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details. To overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and

A comparison of maximum likelihood and other estimators of eigenvalues from several correlated Monte Carlo samples

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

Beer, M.

1980-12-01

The maximum likelihood method for the multivariate normal distribution is applied to the case of several individual eigenvalues. Correlated Monte Carlo estimates of the eigenvalue are assumed to follow this prescription and aspects of the assumption are examined. Monte Carlo cell calculations using the SAM-CE and VIM codes for the TRX-1 and TRX-2 benchmark reactors, and SAM-CE full core results are analyzed with this method. Variance reductions of a few percent to a factor of 2 are obtained from maximum likelihood estimation as compared with the simple average and the minimum variance individual eigenvalue. The numerical results verify that themore » use of sample variances and correlation coefficients in place of the corresponding population statistics still leads to nearly minimum variance estimation for a sufficient number of histories and aggregates.« less

Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets

PubMed Central

Zhou, Xiaofan; Shen, Xing-Xing; Hittinger, Chris Todd

2018-01-01

Abstract The sizes of the data matrices assembled to resolve branches of the tree of life have increased dramatically, motivating the development of programs for fast, yet accurate, inference. For example, several different fast programs have been developed in the very popular maximum likelihood framework, including RAxML/ExaML, PhyML, IQ-TREE, and FastTree. Although these programs are widely used, a systematic evaluation and comparison of their performance using empirical genome-scale data matrices has so far been lacking. To address this question, we evaluated these four programs on 19 empirical phylogenomic data sets with hundreds to thousands of genes and up to 200 taxa with respect to likelihood maximization, tree topology, and computational speed. For single-gene tree inference, we found that the more exhaustive and slower strategies (ten searches per alignment) outperformed faster strategies (one tree search per alignment) using RAxML, PhyML, or IQ-TREE. Interestingly, single-gene trees inferred by the three programs yielded comparable coalescent-based species tree estimations. For concatenation-based species tree inference, IQ-TREE consistently achieved the best-observed likelihoods for all data sets, and RAxML/ExaML was a close second. In contrast, PhyML often failed to complete concatenation-based analyses, whereas FastTree was the fastest but generated lower likelihood values and more dissimilar tree topologies in both types of analyses. Finally, data matrix properties, such as the number of taxa and the strength of phylogenetic signal, sometimes substantially influenced the programs’ relative performance. Our results provide real-world gene and species tree phylogenetic inference benchmarks to inform the design and execution of large-scale phylogenomic data analyses. PMID:29177474

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Accuracy of maximum likelihood estimates of a two-state model in single-molecule FRET

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

Gopich, Irina V.

2015-01-21

Photon sequences from single-molecule Förster resonance energy transfer (FRET) experiments can be analyzed using a maximum likelihood method. Parameters of the underlying kinetic model (FRET efficiencies of the states and transition rates between conformational states) are obtained by maximizing the appropriate likelihood function. In addition, the errors (uncertainties) of the extracted parameters can be obtained from the curvature of the likelihood function at the maximum. We study the standard deviations of the parameters of a two-state model obtained from photon sequences with recorded colors and arrival times. The standard deviations can be obtained analytically in a special case when themore » FRET efficiencies of the states are 0 and 1 and in the limiting cases of fast and slow conformational dynamics. These results are compared with the results of numerical simulations. The accuracy and, therefore, the ability to predict model parameters depend on how fast the transition rates are compared to the photon count rate. In the limit of slow transitions, the key parameters that determine the accuracy are the number of transitions between the states and the number of independent photon sequences. In the fast transition limit, the accuracy is determined by the small fraction of photons that are correlated with their neighbors. The relative standard deviation of the relaxation rate has a “chevron” shape as a function of the transition rate in the log-log scale. The location of the minimum of this function dramatically depends on how well the FRET efficiencies of the states are separated.« less

Accuracy of maximum likelihood estimates of a two-state model in single-molecule FRET

PubMed Central

Gopich, Irina V.

2015-01-01

Photon sequences from single-molecule Förster resonance energy transfer (FRET) experiments can be analyzed using a maximum likelihood method. Parameters of the underlying kinetic model (FRET efficiencies of the states and transition rates between conformational states) are obtained by maximizing the appropriate likelihood function. In addition, the errors (uncertainties) of the extracted parameters can be obtained from the curvature of the likelihood function at the maximum. We study the standard deviations of the parameters of a two-state model obtained from photon sequences with recorded colors and arrival times. The standard deviations can be obtained analytically in a special case when the FRET efficiencies of the states are 0 and 1 and in the limiting cases of fast and slow conformational dynamics. These results are compared with the results of numerical simulations. The accuracy and, therefore, the ability to predict model parameters depend on how fast the transition rates are compared to the photon count rate. In the limit of slow transitions, the key parameters that determine the accuracy are the number of transitions between the states and the number of independent photon sequences. In the fast transition limit, the accuracy is determined by the small fraction of photons that are correlated with their neighbors. The relative standard deviation of the relaxation rate has a “chevron” shape as a function of the transition rate in the log-log scale. The location of the minimum of this function dramatically depends on how well the FRET efficiencies of the states are separated. PMID:25612692

Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm

PubMed Central

Hesterman, Jacob Y.; Caucci, Luca; Kupinski, Matthew A.; Barrett, Harrison H.; Furenlid, Lars R.

2010-01-01

A fast search algorithm capable of operating in multi-dimensional spaces is introduced. As a sample application, we demonstrate its utility in the 2D and 3D maximum-likelihood position-estimation problem that arises in the processing of PMT signals to derive interaction locations in compact gamma cameras. We demonstrate that the algorithm can be parallelized in pipelines, and thereby efficiently implemented in specialized hardware, such as field-programmable gate arrays (FPGAs). A 2D implementation of the algorithm is achieved in Cell/BE processors, resulting in processing speeds above one million events per second, which is a 20× increase in speed over a conventional desktop machine. Graphics processing units (GPUs) are used for a 3D application of the algorithm, resulting in processing speeds of nearly 250,000 events per second which is a 250× increase in speed over a conventional desktop machine. These implementations indicate the viability of the algorithm for use in real-time imaging applications. PMID:20824155

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044

Optimization of a Nucleic Acids united-RESidue 2-Point model (NARES-2P) with a maximum-likelihood approach

NASA Astrophysics Data System (ADS)

He, Yi; Liwo, Adam; Scheraga, Harold A.

2015-12-01

Coarse-grained models are useful tools to investigate the structural and thermodynamic properties of biomolecules. They are obtained by merging several atoms into one interaction site. Such simplified models try to capture as much as possible information of the original biomolecular system in all-atom representation but the resulting parameters of these coarse-grained force fields still need further optimization. In this paper, a force field optimization method, which is based on maximum-likelihood fitting of the simulated to the experimental conformational ensembles and least-squares fitting of the simulated to the experimental heat-capacity curves, is applied to optimize the Nucleic Acid united-RESidue 2-point (NARES-2P) model for coarse-grained simulations of nucleic acids recently developed in our laboratory. The optimized NARES-2P force field reproduces the structural and thermodynamic data of small DNA molecules much better than the original force field.

Pointwise nonparametric maximum likelihood estimator of stochastically ordered survivor functions

PubMed Central

Park, Yongseok; Taylor, Jeremy M. G.; Kalbfleisch, John D.

2012-01-01

In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise constrained nonparametric maximum likelihood estimator, which is defined at each time t by the estimates of the survivor functions subject to constraints applied at time t only. We also propose an efficient method to obtain the estimator. The estimator of each constrained survivor function is shown to be nonincreasing in t, and its consistency and asymptotic distribution are established. A simulation study suggests better small and large sample properties than for alternative estimators. An example using prostate cancer data illustrates the method. PMID:23843661

Asymptotic Properties of Induced Maximum Likelihood Estimates of Nonlinear Models for Item Response Variables: The Finite-Generic-Item-Pool Case.

ERIC Educational Resources Information Center

Jones, Douglas H.

The progress of modern mental test theory depends very much on the techniques of maximum likelihood estimation, and many popular applications make use of likelihoods induced by logistic item response models. While, in reality, item responses are nonreplicate within a single examinee and the logistic models are only ideal, practitioners make…

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

PubMed Central

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A.

2012-01-01

This paper considers semiparametric estimation of the Cox proportional hazards model for right-censored and length-biased data arising from prevalent sampling. To exploit the special structure of length-biased sampling, we propose a maximum pseudo-profile likelihood estimator, which can handle time-dependent covariates and is consistent under covariate-dependent censoring. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates the methods and theory. PMID:23843659

A maximum likelihood analysis of the CoGeNT public dataset

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

Kelso, Chris, E-mail: ckelso@unf.edu

The CoGeNT detector, located in the Soudan Underground Laboratory in Northern Minnesota, consists of a 475 grams (fiducial mass of 330 grams) target mass of p-type point contact germanium detector that measures the ionization charge created by nuclear recoils. This detector has searched for recoils created by dark matter since December of 2009. We analyze the public dataset from the CoGeNT experiment to search for evidence of dark matter interactions with the detector. We perform an unbinned maximum likelihood fit to the data and compare the significance of different WIMP hypotheses relative to each other and the null hypothesis ofmore » no WIMP interactions. This work presents the current status of the analysis.« less

Maximum likelihood sequence estimation for optical complex direct modulation.

PubMed

Che, Di; Yuan, Feng; Shieh, William

2017-04-17

Semiconductor lasers are versatile optical transmitters in nature. Through the direct modulation (DM), the intensity modulation is realized by the linear mapping between the injection current and the light power, while various angle modulations are enabled by the frequency chirp. Limited by the direct detection, DM lasers used to be exploited only as 1-D (intensity or angle) transmitters by suppressing or simply ignoring the other modulation. Nevertheless, through the digital coherent detection, simultaneous intensity and angle modulations (namely, 2-D complex DM, CDM) can be realized by a single laser diode. The crucial technique of CDM is the joint demodulation of intensity and differential phase with the maximum likelihood sequence estimation (MLSE), supported by a closed-form discrete signal approximation of frequency chirp to characterize the MLSE transition probability. This paper proposes a statistical method for the transition probability to significantly enhance the accuracy of the chirp model. Using the statistical estimation, we demonstrate the first single-channel 100-Gb/s PAM-4 transmission over 1600-km fiber with only 10G-class DM lasers.

Optimal designs based on the maximum quasi-likelihood estimator

PubMed Central

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

2016-01-01

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

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

Neandertal admixture in Eurasia confirmed by maximum-likelihood analysis of three genomes.

PubMed

Lohse, Konrad; Frantz, Laurent A F

2014-04-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4-7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination.

Maximum likelihood inference implies a high, not a low, ancestral haploid chromosome number in Araceae, with a critique of the bias introduced by ‘x’

PubMed Central

Cusimano, Natalie; Sousa, Aretuza; Renner, Susanne S.

2012-01-01

Background and Aims For 84 years, botanists have relied on calculating the highest common factor for series of haploid chromosome numbers to arrive at a so-called basic number, x. This was done without consistent (reproducible) reference to species relationships and frequencies of different numbers in a clade. Likelihood models that treat polyploidy, chromosome fusion and fission as events with particular probabilities now allow reconstruction of ancestral chromosome numbers in an explicit framework. We have used a modelling approach to reconstruct chromosome number change in the large monocot family Araceae and to test earlier hypotheses about basic numbers in the family. Methods Using a maximum likelihood approach and chromosome counts for 26 % of the 3300 species of Araceae and representative numbers for each of the other 13 families of Alismatales, polyploidization events and single chromosome changes were inferred on a genus-level phylogenetic tree for 113 of the 117 genera of Araceae. Key Results The previously inferred basic numbers x = 14 and x = 7 are rejected. Instead, maximum likelihood optimization revealed an ancestral haploid chromosome number of n = 16, Bayesian inference of n = 18. Chromosome fusion (loss) is the predominant inferred event, whereas polyploidization events occurred less frequently and mainly towards the tips of the tree. Conclusions The bias towards low basic numbers (x) introduced by the algebraic approach to inferring chromosome number changes, prevalent among botanists, may have contributed to an unrealistic picture of ancestral chromosome numbers in many plant clades. The availability of robust quantitative methods for reconstructing ancestral chromosome numbers on molecular phylogenetic trees (with or without branch length information), with confidence statistics, makes the calculation of x an obsolete approach, at least when applied to large clades. PMID:22210850

Maximum-likelihood estimation of parameterized wavefronts from multifocal data

PubMed Central

Sakamoto, Julia A.; Barrett, Harrison H.

2012-01-01

A method for determining the pupil phase distribution of an optical system is demonstrated. Coefficients in a wavefront expansion were estimated using likelihood methods, where the data consisted of multiple irradiance patterns near focus. Proof-of-principle results were obtained in both simulation and experiment. Large-aberration wavefronts were handled in the numerical study. Experimentally, we discuss the handling of nuisance parameters. Fisher information matrices, Cramér-Rao bounds, and likelihood surfaces are examined. ML estimates were obtained by simulated annealing to deal with numerous local extrema in the likelihood function. Rapid processing techniques were employed to reduce the computational time. PMID:22772282

Numerical Experimentation with Maximum Likelihood Identification in Static Distributed Systems

NASA Technical Reports Server (NTRS)

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

1985-01-01

Many important issues in the control of large space structures are intimately related to the fundamental problem of parameter identification. One might also ask how well this identification process can be carried out in the presence of noisy data since no sensor system is perfect. With these considerations in mind the algorithms herein are designed to treat both the case of uncertainties in the modeling and uncertainties in the data. The analytical aspects of maximum likelihood identification are considered in some detail in another paper. The questions relevant to the implementation of these schemes are dealt with, particularly as they apply to models of large space structures. The emphasis is on the influence of the infinite dimensional character of the problem on finite dimensional implementations of the algorithms. Those areas of current and future analysis are highlighted which indicate the interplay between error analysis and possible truncations of the state and parameter spaces.

Maximum Likelihood Analysis of Low Energy CDMS II Germanium Data

DOE PAGES

Agnese, R.

2015-03-30

We report on the results of a search for a Weakly Interacting Massive Particle (WIMP) signal in low-energy data of the Cryogenic Dark Matter Search experiment using a maximum likelihood analysis. A background model is constructed using GEANT4 to simulate the surface-event background from Pb210decay-chain events, while using independent calibration data to model the gamma background. Fitting this background model to the data results in no statistically significant WIMP component. In addition, we also perform fits using an analytic ad hoc background model proposed by Collar and Fields, who claimed to find a large excess of signal-like events in ourmore » data. Finally, we confirm the strong preference for a signal hypothesis in their analysis under these assumptions, but excesses are observed in both single- and multiple-scatter events, which implies the signal is not caused by WIMPs, but rather reflects the inadequacy of their background model.« less

A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.

PubMed

Balzer, Laura B; Zheng, Wenjing; van der Laan, Mark J; Petersen, Maya L

2018-01-01

We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual's covariates on another's outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment.

The Benefits of Maximum Likelihood Estimators in Predicting Bulk Permeability and Upscaling Fracture Networks

NASA Astrophysics Data System (ADS)

Emanuele Rizzo, Roberto; Healy, David; De Siena, Luca

2016-04-01

The success of any predictive model is largely dependent on the accuracy with which its parameters are known. When characterising fracture networks in fractured rock, one of the main issues is accurately scaling the parameters governing the distribution of fracture attributes. Optimal characterisation and analysis of fracture attributes (lengths, apertures, orientations and densities) is fundamental to the estimation of permeability and fluid flow, which are of primary importance in a number of contexts including: hydrocarbon production from fractured reservoirs; geothermal energy extraction; and deeper Earth systems, such as earthquakes and ocean floor hydrothermal venting. Our work links outcrop fracture data to modelled fracture networks in order to numerically predict bulk permeability. We collected outcrop data from a highly fractured upper Miocene biosiliceous mudstone formation, cropping out along the coastline north of Santa Cruz (California, USA). Using outcrop fracture networks as analogues for subsurface fracture systems has several advantages, because key fracture attributes such as spatial arrangements and lengths can be effectively measured only on outcrops [1]. However, a limitation when dealing with outcrop data is the relative sparseness of natural data due to the intrinsic finite size of the outcrops. We make use of a statistical approach for the overall workflow, starting from data collection with the Circular Windows Method [2]. Then we analyse the data statistically using Maximum Likelihood Estimators, which provide greater accuracy compared to the more commonly used Least Squares linear regression when investigating distribution of fracture attributes. Finally, we estimate the bulk permeability of the fractured rock mass using Oda's tensorial approach [3]. The higher quality of this statistical analysis is fundamental: better statistics of the fracture attributes means more accurate permeability estimation, since the fracture attributes feed

Maximum likelihood positioning algorithm for high-resolution PET scanners

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

Gross-Weege, Nicolas, E-mail: nicolas.gross-weege@pmi.rwth-aachen.de, E-mail: schulz@pmi.rwth-aachen.de; Schug, David; Hallen, Patrick

2016-06-15

Purpose: In high-resolution positron emission tomography (PET), lightsharing elements are incorporated into typical detector stacks to read out scintillator arrays in which one scintillator element (crystal) is smaller than the size of the readout channel. In order to identify the hit crystal by means of the measured light distribution, a positioning algorithm is required. One commonly applied positioning algorithm uses the center of gravity (COG) of the measured light distribution. The COG algorithm is limited in spatial resolution by noise and intercrystal Compton scatter. The purpose of this work is to develop a positioning algorithm which overcomes this limitation. Methods:more » The authors present a maximum likelihood (ML) algorithm which compares a set of expected light distributions given by probability density functions (PDFs) with the measured light distribution. Instead of modeling the PDFs by using an analytical model, the PDFs of the proposed ML algorithm are generated assuming a single-gamma-interaction model from measured data. The algorithm was evaluated with a hot-rod phantom measurement acquired with the preclinical HYPERION II {sup D} PET scanner. In order to assess the performance with respect to sensitivity, energy resolution, and image quality, the ML algorithm was compared to a COG algorithm which calculates the COG from a restricted set of channels. The authors studied the energy resolution of the ML and the COG algorithm regarding incomplete light distributions (missing channel information caused by detector dead time). Furthermore, the authors investigated the effects of using a filter based on the likelihood values on sensitivity, energy resolution, and image quality. Results: A sensitivity gain of up to 19% was demonstrated in comparison to the COG algorithm for the selected operation parameters. Energy resolution and image quality were on a similar level for both algorithms. Additionally, the authors demonstrated that the performance

Single particle maximum likelihood reconstruction from superresolution microscopy images

PubMed Central

Verdier, Timothée; Gunzenhauser, Julia; Manley, Suliana; Castelnovo, Martin

2017-01-01

Point localization superresolution microscopy enables fluorescently tagged molecules to be imaged beyond the optical diffraction limit, reaching single molecule localization precisions down to a few nanometers. For small objects whose sizes are few times this precision, localization uncertainty prevents the straightforward extraction of a structural model from the reconstructed images. We demonstrate in the present work that this limitation can be overcome at the single particle level, requiring no particle averaging, by using a maximum likelihood reconstruction (MLR) method perfectly suited to the stochastic nature of such superresolution imaging. We validate this method by extracting structural information from both simulated and experimental PALM data of immature virus-like particles of the Human Immunodeficiency Virus (HIV-1). MLR allows us to measure the radii of individual viruses with precision of a few nanometers and confirms the incomplete closure of the viral protein lattice. The quantitative results of our analysis are consistent with previous cryoelectron microscopy characterizations. Our study establishes the framework for a method that can be broadly applied to PALM data to determine the structural parameters for an existing structural model, and is particularly well suited to heterogeneous features due to its single particle implementation. PMID:28253349

Neandertal Admixture in Eurasia Confirmed by Maximum-Likelihood Analysis of Three Genomes

PubMed Central

Lohse, Konrad; Frantz, Laurent A. F.

2014-01-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4−7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination. PMID:24532731

ReplacementMatrix: a web server for maximum-likelihood estimation of amino acid replacement rate matrices.

PubMed

Dang, Cuong Cao; Lefort, Vincent; Le, Vinh Sy; Le, Quang Si; Gascuel, Olivier

2011-10-01

Amino acid replacement rate matrices are an essential basis of protein studies (e.g. in phylogenetics and alignment). A number of general purpose matrices have been proposed (e.g. JTT, WAG, LG) since the seminal work of Margaret Dayhoff and co-workers. However, it has been shown that matrices specific to certain protein groups (e.g. mitochondrial) or life domains (e.g. viruses) differ significantly from general average matrices, and thus perform better when applied to the data to which they are dedicated. This Web server implements the maximum-likelihood estimation procedure that was used to estimate LG, and provides a number of tools and facilities. Users upload a set of multiple protein alignments from their domain of interest and receive the resulting matrix by email, along with statistics and comparisons with other matrices. A non-parametric bootstrap is performed optionally to assess the variability of replacement rate estimates. Maximum-likelihood trees, inferred using the estimated rate matrix, are also computed optionally for each input alignment. Finely tuned procedures and up-to-date ML software (PhyML 3.0, XRATE) are combined to perform all these heavy calculations on our clusters. http://www.atgc-montpellier.fr/ReplacementMatrix/ olivier.gascuel@lirmm.fr Supplementary data are available at http://www.atgc-montpellier.fr/ReplacementMatrix/

Spectral identification of a 90Sr source in the presence of masking nuclides using Maximum-Likelihood deconvolution

NASA Astrophysics Data System (ADS)

Neuer, Marcus J.

2013-11-01

A technique for the spectral identification of strontium-90 is shown, utilising a Maximum-Likelihood deconvolution. Different deconvolution approaches are discussed and summarised. Based on the intensity distribution of the beta emission and Geant4 simulations, a combined response matrix is derived, tailored to the β- detection process in sodium iodide detectors. It includes scattering effects and attenuation by applying a base material decomposition extracted from Geant4 simulations with a CAD model for a realistic detector system. Inversion results of measurements show the agreement between deconvolution and reconstruction. A detailed investigation with additional masking sources like 40K, 226Ra and 131I shows that a contamination of strontium can be found in the presence of these nuisance sources. Identification algorithms for strontium are presented based on the derived technique. For the implementation of blind identification, an exemplary masking ratio is calculated.

RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

PubMed

Stamatakis, Alexandros

2006-11-01

RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak

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

PubMed

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

2010-03-01

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

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

NASA Technical Reports Server (NTRS)

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

1977-01-01

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

Procedure for estimating stability and control parameters from flight test data by using maximum likelihood methods employing a real-time digital system

NASA Technical Reports Server (NTRS)

Grove, R. D.; Bowles, R. L.; Mayhew, S. C.

1972-01-01

A maximum likelihood parameter estimation procedure and program were developed for the extraction of the stability and control derivatives of aircraft from flight test data. Nonlinear six-degree-of-freedom equations describing aircraft dynamics were used to derive sensitivity equations for quasilinearization. The maximum likelihood function with quasilinearization was used to derive the parameter change equations, the covariance matrices for the parameters and measurement noise, and the performance index function. The maximum likelihood estimator was mechanized into an iterative estimation procedure utilizing a real time digital computer and graphic display system. This program was developed for 8 measured state variables and 40 parameters. Test cases were conducted with simulated data for validation of the estimation procedure and program. The program was applied to a V/STOL tilt wing aircraft, a military fighter airplane, and a light single engine airplane. The particular nonlinear equations of motion, derivation of the sensitivity equations, addition of accelerations into the algorithm, operational features of the real time digital system, and test cases are described.

Multilevel modeling of single-case data: A comparison of maximum likelihood and Bayesian estimation.

PubMed

Moeyaert, Mariola; Rindskopf, David; Onghena, Patrick; Van den Noortgate, Wim

2017-12-01

The focus of this article is to describe Bayesian estimation, including construction of prior distributions, and to compare parameter recovery under the Bayesian framework (using weakly informative priors) and the maximum likelihood (ML) framework in the context of multilevel modeling of single-case experimental data. Bayesian estimation results were found similar to ML estimation results in terms of the treatment effect estimates, regardless of the functional form and degree of information included in the prior specification in the Bayesian framework. In terms of the variance component estimates, both the ML and Bayesian estimation procedures result in biased and less precise variance estimates when the number of participants is small (i.e., 3). By increasing the number of participants to 5 or 7, the relative bias is close to 5% and more precise estimates are obtained for all approaches, except for the inverse-Wishart prior using the identity matrix. When a more informative prior was added, more precise estimates for the fixed effects and random effects were obtained, even when only 3 participants were included. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Maximum likelihood estimation of signal detection model parameters for the assessment of two-stage diagnostic strategies.

PubMed

Lirio, R B; Dondériz, I C; Pérez Abalo, M C

1992-08-01

The methodology of Receiver Operating Characteristic curves based on the signal detection model is extended to evaluate the accuracy of two-stage diagnostic strategies. A computer program is developed for the maximum likelihood estimation of parameters that characterize the sensitivity and specificity of two-stage classifiers according to this extended methodology. Its use is briefly illustrated with data collected in a two-stage screening for auditory defects.

Multifrequency InSAR height reconstruction through maximum likelihood estimation of local planes parameters.

PubMed

Pascazio, Vito; Schirinzi, Gilda

2002-01-01

In this paper, a technique that is able to reconstruct highly sloped and discontinuous terrain height profiles, starting from multifrequency wrapped phase acquired by interferometric synthetic aperture radar (SAR) systems, is presented. We propose an innovative unwrapping method, based on a maximum likelihood estimation technique, which uses multifrequency independent phase data, obtained by filtering the interferometric SAR raw data pair through nonoverlapping band-pass filters, and approximating the unknown surface by means of local planes. Since the method does not exploit the phase gradient, it assures the uniqueness of the solution, even in the case of highly sloped or piecewise continuous elevation patterns with strong discontinuities.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3; A Recursive Maximum Likelihood Decoding

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc

1998-01-01

The Viterbi algorithm is indeed a very simple and efficient method of implementing the maximum likelihood decoding. However, if we take advantage of the structural properties in a trellis section, other efficient trellis-based decoding algorithms can be devised. Recently, an efficient trellis-based recursive maximum likelihood decoding (RMLD) algorithm for linear block codes has been proposed. This algorithm is more efficient than the conventional Viterbi algorithm in both computation and hardware requirements. Most importantly, the implementation of this algorithm does not require the construction of the entire code trellis, only some special one-section trellises of relatively small state and branch complexities are needed for constructing path (or branch) metric tables recursively. At the end, there is only one table which contains only the most likely code-word and its metric for a given received sequence r = (r(sub 1), r(sub 2),...,r(sub n)). This algorithm basically uses the divide and conquer strategy. Furthermore, it allows parallel/pipeline processing of received sequences to speed up decoding.

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory.

ERIC Educational Resources Information Center

Kelderman, Henk

1992-01-01

Describes algorithms used in the computer program LOGIMO for obtaining maximum likelihood estimates of the parameters in loglinear models. These algorithms are also useful for the analysis of loglinear item-response theory models. Presents modified versions of the iterative proportional fitting and Newton-Raphson algorithms. Simulated data…

Maximum likelihood estimation for semiparametric transformation models with interval-censored data

PubMed Central

Mao, Lu; Lin, D. Y.

2016-01-01

Abstract Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand. PMID:27279656

An application of the Krylov-FSP-SSA method to parameter fitting with maximum likelihood

NASA Astrophysics Data System (ADS)

Dinh, Khanh N.; Sidje, Roger B.

2017-12-01

Monte Carlo methods such as the stochastic simulation algorithm (SSA) have traditionally been employed in gene regulation problems. However, there has been increasing interest to directly obtain the probability distribution of the molecules involved by solving the chemical master equation (CME). This requires addressing the curse of dimensionality that is inherent in most gene regulation problems. The finite state projection (FSP) seeks to address the challenge and there have been variants that further reduce the size of the projection or that accelerate the resulting matrix exponential. The Krylov-FSP-SSA variant has proved numerically efficient by combining, on one hand, the SSA to adaptively drive the FSP, and on the other hand, adaptive Krylov techniques to evaluate the matrix exponential. Here we apply this Krylov-FSP-SSA to a mutual inhibitory gene network synthetically engineered in Saccharomyces cerevisiae, in which bimodality arises. We show numerically that the approach can efficiently approximate the transient probability distribution, and this has important implications for parameter fitting, where the CME has to be solved for many different parameter sets. The fitting scheme amounts to an optimization problem of finding the parameter set so that the transient probability distributions fit the observations with maximum likelihood. We compare five optimization schemes for this difficult problem, thereby providing further insights into this approach of parameter estimation that is often applied to models in systems biology where there is a need to calibrate free parameters. Work supported by NSF grant DMS-1320849.

Analysis of crackling noise using the maximum-likelihood method: Power-law mixing and exponential damping.

PubMed

Salje, Ekhard K H; Planes, Antoni; Vives, Eduard

2017-10-01

Crackling noise can be initiated by competing or coexisting mechanisms. These mechanisms can combine to generate an approximate scale invariant distribution that contains two or more contributions. The overall distribution function can be analyzed, to a good approximation, using maximum-likelihood methods and assuming that it follows a power law although with nonuniversal exponents depending on a varying lower cutoff. We propose that such distributions are rather common and originate from a simple superposition of crackling noise distributions or exponential damping.

Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.

PubMed

Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi

2015-07-01

Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.

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

PubMed

Gil, Manuel

2014-01-01

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

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

PubMed Central

2014-01-01

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

On the Performance of Maximum Likelihood versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA

ERIC Educational Resources Information Center

Beauducel, Andre; Herzberg, Philipp Yorck

2006-01-01

This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no…

Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient.

PubMed

Bian, Liheng; Suo, Jinli; Chung, Jaebum; Ou, Xiaoze; Yang, Changhuei; Chen, Feng; Dai, Qionghai

2016-06-10

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample's high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal. Results on both simulated data and real data captured using our laser-illuminated FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use.

Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography.

PubMed

Yang, Defu; Wang, Lin; Chen, Dongmei; Yan, Chenggang; He, Xiaowei; Liang, Jimin; Chen, Xueli

2018-05-17

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SP N ) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT.

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

PubMed

Haker, Steven; Wells, William M; Warfield, Simon K; Talos, Ion-Florin; Bhagwat, Jui G; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H

2005-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation*

PubMed Central

Haker, Steven; Wells, William M.; Warfield, Simon K.; Talos, Ion-Florin; Bhagwat, Jui G.; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H.

2010-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging. PMID:16685884

ARMA-Based SEM When the Number of Time Points T Exceeds the Number of Cases N: Raw Data Maximum Likelihood.

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2003-01-01

Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)

Application of maximum-likelihood estimation in optical coherence tomography for nanometer-class thickness estimation

NASA Astrophysics Data System (ADS)

Huang, Jinxin; Yuan, Qun; Tankam, Patrice; Clarkson, Eric; Kupinski, Matthew; Hindman, Holly B.; Aquavella, James V.; Rolland, Jannick P.

2015-03-01

In biophotonics imaging, one important and quantitative task is layer-thickness estimation. In this study, we investigate the approach of combining optical coherence tomography and a maximum-likelihood (ML) estimator for layer thickness estimation in the context of tear film imaging. The motivation of this study is to extend our understanding of tear film dynamics, which is the prerequisite to advance the management of Dry Eye Disease, through the simultaneous estimation of the thickness of the tear film lipid and aqueous layers. The estimator takes into account the different statistical processes associated with the imaging chain. We theoretically investigated the impact of key system parameters, such as the axial point spread functions (PSF) and various sources of noise on measurement uncertainty. Simulations show that an OCT system with a 1 μm axial PSF (FWHM) allows unbiased estimates down to nanometers with nanometer precision. In implementation, we built a customized Fourier domain OCT system that operates in the 600 to 1000 nm spectral window and achieves 0.93 micron axial PSF in corneal epithelium. We then validated the theoretical framework with physical phantoms made of custom optical coatings, with layer thicknesses from tens of nanometers to microns. Results demonstrate unbiased nanometer-class thickness estimates in three different physical phantoms.

Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments

PubMed Central

Eisenhauer, Philipp; Heckman, James J.; Mosso, Stefano

2015-01-01

We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in the 1980s and early 1990s. We use estimates from our model to simulate a synthetic dataset and assess the ability of ML and SMM to recover the model parameters on this sample. We investigate the performance of alternative tuning parameters for SMM. PMID:26494926

Efficient Levenberg-Marquardt minimization of the maximum likelihood estimator for Poisson deviates

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

Laurence, T; Chromy, B

2009-11-10

Histograms of counted events are Poisson distributed, but are typically fitted without justification using nonlinear least squares fitting. The more appropriate maximum likelihood estimator (MLE) for Poisson distributed data is seldom used. We extend the use of the Levenberg-Marquardt algorithm commonly used for nonlinear least squares minimization for use with the MLE for Poisson distributed data. In so doing, we remove any excuse for not using this more appropriate MLE. We demonstrate the use of the algorithm and the superior performance of the MLE using simulations and experiments in the context of fluorescence lifetime imaging. Scientists commonly form histograms ofmore » counted events from their data, and extract parameters by fitting to a specified model. Assuming that the probability of occurrence for each bin is small, event counts in the histogram bins will be distributed according to the Poisson distribution. We develop here an efficient algorithm for fitting event counting histograms using the maximum likelihood estimator (MLE) for Poisson distributed data, rather than the non-linear least squares measure. This algorithm is a simple extension of the common Levenberg-Marquardt (L-M) algorithm, is simple to implement, quick and robust. Fitting using a least squares measure is most common, but it is the maximum likelihood estimator only for Gaussian-distributed data. Non-linear least squares methods may be applied to event counting histograms in cases where the number of events is very large, so that the Poisson distribution is well approximated by a Gaussian. However, it is not easy to satisfy this criterion in practice - which requires a large number of events. It has been well-known for years that least squares procedures lead to biased results when applied to Poisson-distributed data; a recent paper providing extensive characterization of these biases in exponential fitting is given. The more appropriate measure based on the maximum likelihood

User's manual for MMLE3, a general FORTRAN program for maximum likelihood parameter estimation

NASA Technical Reports Server (NTRS)

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

1980-01-01

A user's manual for the FORTRAN IV computer program MMLE3 is described. It is a maximum likelihood parameter estimation program capable of handling general bilinear dynamic equations of arbitrary order with measurement noise and/or state noise (process noise). The theory and use of the program is described. The basic MMLE3 program is quite general and, therefore, applicable to a wide variety of problems. The basic program can interact with a set of user written problem specific routines to simplify the use of the program on specific systems. A set of user routines for the aircraft stability and control derivative estimation problem is provided with the program.

Impact of Violation of the Missing-at-Random Assumption on Full-Information Maximum Likelihood Method in Multidimensional Adaptive Testing

ERIC Educational Resources Information Center

Han, Kyung T.; Guo, Fanmin

2014-01-01

The full-information maximum likelihood (FIML) method makes it possible to estimate and analyze structural equation models (SEM) even when data are partially missing, enabling incomplete data to contribute to model estimation. The cornerstone of FIML is the missing-at-random (MAR) assumption. In (unidimensional) computerized adaptive testing…

Do-it-yourself statistics: A computer-assisted likelihood approach to analysis of data from genetic crosses.

PubMed Central

Robbins, L G

2000-01-01

Graduate school programs in genetics have become so full that courses in statistics have often been eliminated. In addition, typical introductory statistics courses for the "statistics user" rather than the nascent statistician are laden with methods for analysis of measured variables while genetic data are most often discrete numbers. These courses are often seen by students and genetics professors alike as largely irrelevant cookbook courses. The powerful methods of likelihood analysis, although commonly employed in human genetics, are much less often used in other areas of genetics, even though current computational tools make this approach readily accessible. This article introduces the MLIKELY.PAS computer program and the logic of do-it-yourself maximum-likelihood statistics. The program itself, course materials, and expanded discussions of some examples that are only summarized here are available at http://www.unisi. it/ricerca/dip/bio_evol/sitomlikely/mlikely.h tml. PMID:10628965

Bit Error Probability for Maximum Likelihood Decoding of Linear Block Codes

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc P. C.; Rhee, Dojun

1996-01-01

In this paper, the bit error probability P(sub b) for maximum likelihood decoding of binary linear codes is investigated. The contribution of each information bit to P(sub b) is considered. For randomly generated codes, it is shown that the conventional approximation at high SNR P(sub b) is approximately equal to (d(sub H)/N)P(sub s), where P(sub s) represents the block error probability, holds for systematic encoding only. Also systematic encoding provides the minimum P(sub b) when the inverse mapping corresponding to the generator matrix of the code is used to retrieve the information sequence. The bit error performances corresponding to other generator matrix forms are also evaluated. Although derived for codes with a generator matrix randomly generated, these results are shown to provide good approximations for codes used in practice. Finally, for decoding methods which require a generator matrix with a particular structure such as trellis decoding or algebraic-based soft decision decoding, equivalent schemes that reduce the bit error probability are discussed.

Pearson-type goodness-of-fit test with bootstrap maximum likelihood estimation.

PubMed

Yin, Guosheng; Ma, Yanyuan

2013-01-01

The Pearson test statistic is constructed by partitioning the data into bins and computing the difference between the observed and expected counts in these bins. If the maximum likelihood estimator (MLE) of the original data is used, the statistic generally does not follow a chi-squared distribution or any explicit distribution. We propose a bootstrap-based modification of the Pearson test statistic to recover the chi-squared distribution. We compute the observed and expected counts in the partitioned bins by using the MLE obtained from a bootstrap sample. This bootstrap-sample MLE adjusts exactly the right amount of randomness to the test statistic, and recovers the chi-squared distribution. The bootstrap chi-squared test is easy to implement, as it only requires fitting exactly the same model to the bootstrap data to obtain the corresponding MLE, and then constructs the bin counts based on the original data. We examine the test size and power of the new model diagnostic procedure using simulation studies and illustrate it with a real data set.

Estimating the Effect of Competition on Trait Evolution Using Maximum Likelihood Inference.

PubMed

Drury, Jonathan; Clavel, Julien; Manceau, Marc; Morlon, Hélène

2016-07-01

Many classical ecological and evolutionary theoretical frameworks posit that competition between species is an important selective force. For example, in adaptive radiations, resource competition between evolving lineages plays a role in driving phenotypic diversification and exploration of novel ecological space. Nevertheless, current models of trait evolution fit to phylogenies and comparative data sets are not designed to incorporate the effect of competition. The most advanced models in this direction are diversity-dependent models where evolutionary rates depend on lineage diversity. However, these models still treat changes in traits in one branch as independent of the value of traits on other branches, thus ignoring the effect of species similarity on trait evolution. Here, we consider a model where the evolutionary dynamics of traits involved in interspecific interactions are influenced by species similarity in trait values and where we can specify which lineages are in sympatry. We develop a maximum likelihood based approach to fit this model to combined phylogenetic and phenotypic data. Using simulations, we demonstrate that the approach accurately estimates the simulated parameter values across a broad range of parameter space. Additionally, we develop tools for specifying the biogeographic context in which trait evolution occurs. In order to compare models, we also apply these biogeographic methods to specify which lineages interact sympatrically for two diversity-dependent models. Finally, we fit these various models to morphological data from a classical adaptive radiation (Greater Antillean Anolis lizards). We show that models that account for competition and geography perform better than other models. The matching competition model is an important new tool for studying the influence of interspecific interactions, in particular competition, on phenotypic evolution. More generally, it constitutes a step toward a better integration of interspecific

Maximum likelihood estimation in calibrating a stereo camera setup.

PubMed

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

1999-02-01

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

Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

PubMed

Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

2012-07-01

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

Maximum likelihood bolometric tomography for the determination of the uncertainties in the radiation emission on JET TOKAMAK

NASA Astrophysics Data System (ADS)

Craciunescu, Teddy; Peluso, Emmanuele; Murari, Andrea; Gelfusa, Michela; JET Contributors

2018-05-01

The total emission of radiation is a crucial quantity to calculate the power balances and to understand the physics of any Tokamak. Bolometric systems are the main tool to measure this important physical quantity through quite sophisticated tomographic inversion methods. On the Joint European Torus, the coverage of the bolometric diagnostic, due to the availability of basically only two projection angles, is quite limited, rendering the inversion a very ill-posed mathematical problem. A new approach, based on the maximum likelihood, has therefore been developed and implemented to alleviate one of the major weaknesses of traditional tomographic techniques: the difficulty to determine routinely the confidence intervals in the results. The method has been validated by numerical simulations with phantoms to assess the quality of the results and to optimise the configuration of the parameters for the main types of emissivity encountered experimentally. The typical levels of statistical errors, which may significantly influence the quality of the reconstructions, have been identified. The systematic tests with phantoms indicate that the errors in the reconstructions are quite limited and their effect on the total radiated power remains well below 10%. A comparison with other approaches to the inversion and to the regularization has also been performed.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

PubMed Central

Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

2011-01-01

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

A comparison of minimum distance and maximum likelihood techniques for proportion estimation

NASA Technical Reports Server (NTRS)

Woodward, W. A.; Schucany, W. R.; Lindsey, H.; Gray, H. L.

1982-01-01

The estimation of mixing proportions P sub 1, P sub 2,...P sub m in the mixture density f(x) = the sum of the series P sub i F sub i(X) with i = 1 to M is often encountered in agricultural remote sensing problems in which case the p sub i's usually represent crop proportions. In these remote sensing applications, component densities f sub i(x) have typically been assumed to be normally distributed, and parameter estimation has been accomplished using maximum likelihood (ML) techniques. Minimum distance (MD) estimation is examined as an alternative to ML where, in this investigation, both procedures are based upon normal components. Results indicate that ML techniques are superior to MD when component distributions actually are normal, while MD estimation provides better estimates than ML under symmetric departures from normality. When component distributions are not symmetric, however, it is seen that neither of these normal based techniques provides satisfactory results.

Profile-likelihood Confidence Intervals in Item Response Theory Models.

PubMed

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

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

NASA Astrophysics Data System (ADS)

Kojima, Yohei; Takeda, Kazuaki; Adachi, Fumiyuki

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

SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction

PubMed Central

Dvornek, Nicha C.; Sigworth, Fred J.; Tagare, Hemant D.

2015-01-01

Single particle reconstruction methods based on the maximum-likelihood principle and the expectation-maximization (E–M) algorithm are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of computational servers. To overcome this computational bottleneck, we propose a new mathematical framework for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude and the proposed algorithm produces similar quality reconstructions compared to the standard maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy (cryo-EM) data and projection images, greatly reducing the number of image transformations and comparisons that are computed. Experiments using simulated and actual cryo-EM data show that speedup in overall execution time compared to traditional maximum-likelihood reconstruction reaches factors of over 300. PMID:25839831

A maximum likelihood method for high resolution proton radiography/proton CT

NASA Astrophysics Data System (ADS)

Collins-Fekete, Charles-Antoine; Brousmiche, Sébastien; Portillo, Stephen K. N.; Beaulieu, Luc; Seco, Joao

2016-12-01

Multiple Coulomb scattering (MCS) is the largest contributor to blurring in proton imaging. In this work, we developed a maximum likelihood least squares estimator that improves proton radiography’s spatial resolution. The water equivalent thickness (WET) through projections defined from the source to the detector pixels were estimated such that they maximizes the likelihood of the energy loss of every proton crossing the volume. The length spent in each projection was calculated through the optimized cubic spline path estimate. The proton radiographies were produced using Geant4 simulations. Three phantoms were studied here: a slanted cube in a tank of water to measure 2D spatial resolution, a voxelized head phantom for clinical performance evaluation as well as a parametric Catphan phantom (CTP528) for 3D spatial resolution. Two proton beam configurations were used: a parallel and a conical beam. Proton beams of 200 and 330 MeV were simulated to acquire the radiography. Spatial resolution is increased from 2.44 lp cm-1 to 4.53 lp cm-1 in the 200 MeV beam and from 3.49 lp cm-1 to 5.76 lp cm-1 in the 330 MeV beam. Beam configurations do not affect the reconstructed spatial resolution as investigated between a radiography acquired with the parallel (3.49 lp cm-1 to 5.76 lp cm-1) or conical beam (from 3.49 lp cm-1 to 5.56 lp cm-1). The improved images were then used as input in a photon tomography algorithm. The proton CT reconstruction of the Catphan phantom shows high spatial resolution (from 2.79 to 5.55 lp cm-1 for the parallel beam and from 3.03 to 5.15 lp cm-1 for the conical beam) and the reconstruction of the head phantom, although qualitative, shows high contrast in the gradient region. The proposed formulation of the optimization demonstrates serious potential to increase the spatial resolution (up by 65 % ) in proton radiography and greatly accelerate proton computed tomography reconstruction.

A maximum likelihood method for high resolution proton radiography/proton CT.

PubMed

Collins-Fekete, Charles-Antoine; Brousmiche, Sébastien; Portillo, Stephen K N; Beaulieu, Luc; Seco, Joao

2016-12-07

Multiple Coulomb scattering (MCS) is the largest contributor to blurring in proton imaging. In this work, we developed a maximum likelihood least squares estimator that improves proton radiography's spatial resolution. The water equivalent thickness (WET) through projections defined from the source to the detector pixels were estimated such that they maximizes the likelihood of the energy loss of every proton crossing the volume. The length spent in each projection was calculated through the optimized cubic spline path estimate. The proton radiographies were produced using Geant4 simulations. Three phantoms were studied here: a slanted cube in a tank of water to measure 2D spatial resolution, a voxelized head phantom for clinical performance evaluation as well as a parametric Catphan phantom (CTP528) for 3D spatial resolution. Two proton beam configurations were used: a parallel and a conical beam. Proton beams of 200 and 330 MeV were simulated to acquire the radiography. Spatial resolution is increased from 2.44 lp cm -1 to 4.53 lp cm -1 in the 200 MeV beam and from 3.49 lp cm -1 to 5.76 lp cm -1 in the 330 MeV beam. Beam configurations do not affect the reconstructed spatial resolution as investigated between a radiography acquired with the parallel (3.49 lp cm -1 to 5.76 lp cm -1 ) or conical beam (from 3.49 lp cm -1 to 5.56 lp cm -1 ). The improved images were then used as input in a photon tomography algorithm. The proton CT reconstruction of the Catphan phantom shows high spatial resolution (from 2.79 to 5.55 lp cm -1 for the parallel beam and from 3.03 to 5.15 lp cm -1 for the conical beam) and the reconstruction of the head phantom, although qualitative, shows high contrast in the gradient region. The proposed formulation of the optimization demonstrates serious potential to increase the spatial resolution (up by 65[Formula: see text]) in proton radiography and greatly accelerate proton computed tomography reconstruction.

Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

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

Hogden, J.

The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation maymore » decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.« less

The admixture maximum likelihood test to test for association between rare variants and disease phenotypes.

PubMed

Tyrer, Jonathan P; Guo, Qi; Easton, Douglas F; Pharoah, Paul D P

2013-06-06

The development of genotyping arrays containing hundreds of thousands of rare variants across the genome and advances in high-throughput sequencing technologies have made feasible empirical genetic association studies to search for rare disease susceptibility alleles. As single variant testing is underpowered to detect associations, the development of statistical methods to combine analysis across variants - so-called "burden tests" - is an area of active research interest. We previously developed a method, the admixture maximum likelihood test, to test multiple, common variants for association with a trait of interest. We have extended this method, called the rare admixture maximum likelihood test (RAML), for the analysis of rare variants. In this paper we compare the performance of RAML with six other burden tests designed to test for association of rare variants. We used simulation testing over a range of scenarios to test the power of RAML compared to the other rare variant association testing methods. These scenarios modelled differences in effect variability, the average direction of effect and the proportion of associated variants. We evaluated the power for all the different scenarios. RAML tended to have the greatest power for most scenarios where the proportion of associated variants was small, whereas SKAT-O performed a little better for the scenarios with a higher proportion of associated variants. The RAML method makes no assumptions about the proportion of variants that are associated with the phenotype of interest or the magnitude and direction of their effect. The method is flexible and can be applied to both dichotomous and quantitative traits and allows for the inclusion of covariates in the underlying regression model. The RAML method performed well compared to the other methods over a wide range of scenarios. Generally power was moderate in most of the scenarios, underlying the need for large sample sizes in any form of association testing.

Extended maximum likelihood halo-independent analysis of dark matter direct detection data

DOE PAGES

Gelmini, Graciela B.; Georgescu, Andreea; Gondolo, Paolo; ...

2015-11-24

We extend and correct a recently proposed maximum-likelihood halo-independent method to analyze unbinned direct dark matter detection data. Instead of the recoil energy as independent variable we use the minimum speed a dark matter particle must have to impart a given recoil energy to a nucleus. This has the advantage of allowing us to apply the method to any type of target composition and interaction, e.g. with general momentum and velocity dependence, and with elastic or inelastic scattering. We prove the method and provide a rigorous statistical interpretation of the results. As first applications, we find that for dark mattermore » particles with elastic spin-independent interactions and neutron to proton coupling ratio f n/f p=-0.7, the WIMP interpretation of the signal observed by CDMS-II-Si is compatible with the constraints imposed by all other experiments with null results. We also find a similar compatibility for exothermic inelastic spin-independent interactions with f n/f p=-0.8.« less

Maximum likelihood orientation estimation of 1-D patterns in Laguerre-Gauss subspaces.

PubMed

Di Claudio, Elio D; Jacovitti, Giovanni; Laurenti, Alberto

2010-05-01

A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family. It is shown in this paper that the conditional ML solution, derived by elimination of the cross section parameters, surprisingly yields the same asymptotic accuracy as the ML solution for known cross section parameters. The accuracy of the conditional ML estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials. A thorough proof of the key relationship between the LG-CH and the 2-D HG expansions is also provided.

Glutamate receptor-channel gating. Maximum likelihood analysis of gigaohm seal recordings from locust muscle.

PubMed Central

Bates, S E; Sansom, M S; Ball, F G; Ramsey, R L; Usherwood, P N

1990-01-01

Gigaohm recordings have been made from glutamate receptor channels in excised, outside-out patches of collagenase-treated locust muscle membrane. The channels in the excised patches exhibit the kinetic state switching first seen in megaohm recordings from intact muscle fibers. Analysis of channel dwell time distributions reveals that the gating mechanism contains at least four open states and at least four closed states. Dwell time autocorrelation function analysis shows that there are at least three gateways linking the open states of the channel with the closed states. A maximum likelihood procedure has been used to fit six different gating models to the single channel data. Of these models, a cooperative model yields the best fit, and accurately predicts most features of the observed channel gating kinetics. PMID:1696510

RY-Coding and Non-Homogeneous Models Can Ameliorate the Maximum-Likelihood Inferences From Nucleotide Sequence Data with Parallel Compositional Heterogeneity.

PubMed

Ishikawa, Sohta A; Inagaki, Yuji; Hashimoto, Tetsuo

2012-01-01

In phylogenetic analyses of nucleotide sequences, 'homogeneous' substitution models, which assume the stationarity of base composition across a tree, are widely used, albeit individual sequences may bear distinctive base frequencies. In the worst-case scenario, a homogeneous model-based analysis can yield an artifactual union of two distantly related sequences that achieved similar base frequencies in parallel. Such potential difficulty can be countered by two approaches, 'RY-coding' and 'non-homogeneous' models. The former approach converts four bases into purine and pyrimidine to normalize base frequencies across a tree, while the heterogeneity in base frequency is explicitly incorporated in the latter approach. The two approaches have been applied to real-world sequence data; however, their basic properties have not been fully examined by pioneering simulation studies. Here, we assessed the performances of the maximum-likelihood analyses incorporating RY-coding and a non-homogeneous model (RY-coding and non-homogeneous analyses) on simulated data with parallel convergence to similar base composition. Both RY-coding and non-homogeneous analyses showed superior performances compared with homogeneous model-based analyses. Curiously, the performance of RY-coding analysis appeared to be significantly affected by a setting of the substitution process for sequence simulation relative to that of non-homogeneous analysis. The performance of a non-homogeneous analysis was also validated by analyzing a real-world sequence data set with significant base heterogeneity.

Modifying high-order aeroelastic math model of a jet transport using maximum likelihood estimation

NASA Technical Reports Server (NTRS)

Anissipour, Amir A.; Benson, Russell A.

1989-01-01

The design of control laws to damp flexible structural modes requires accurate math models. Unlike the design of control laws for rigid body motion (e.g., where robust control is used to compensate for modeling inaccuracies), structural mode damping usually employs narrow band notch filters. In order to obtain the required accuracy in the math model, maximum likelihood estimation technique is employed to improve the accuracy of the math model using flight data. Presented here are all phases of this methodology: (1) pre-flight analysis (i.e., optimal input signal design for flight test, sensor location determination, model reduction technique, etc.), (2) data collection and preprocessing, and (3) post-flight analysis (i.e., estimation technique and model verification). In addition, a discussion is presented of the software tools used and the need for future study in this field.

Recovery of Item Parameters in the Nominal Response Model: A Comparison of Marginal Maximum Likelihood Estimation and Markov Chain Monte Carlo Estimation.

ERIC Educational Resources Information Center

Wollack, James A.; Bolt, Daniel M.; Cohen, Allan S.; Lee, Young-Sun

2002-01-01

Compared the quality of item parameter estimates for marginal maximum likelihood (MML) and Markov Chain Monte Carlo (MCMC) with the nominal response model using simulation. The quality of item parameter recovery was nearly identical for MML and MCMC, and both methods tended to produce good estimates. (SLD)

Iterative Procedures for Exact Maximum Likelihood Estimation in the First-Order Gaussian Moving Average Model

DTIC Science & Technology

1990-11-01

1 = Q- 1 - 1 QlaaQ- 1.1 + a’Q-1a This is a simple case of a general formula called Woodbury’s formula by some authors; see, for example, Phadke and...1 2. The First-Order Moving Average Model ..... .................. 3. Some Approaches to the Iterative...the approximate likelihood function in some time series models. Useful suggestions have been the Cholesky decomposition of the covariance matrix and

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure.

PubMed

Shen, Yi; Dai, Wei; Richards, Virginia M

2015-03-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given.

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure

PubMed Central

Richards, V. M.; Dai, W.

2014-01-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given. PMID:24671826

Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation, BSEM, and ESEM/EFA Bifactor Approaches.

PubMed

Murray, Aja Louise; Booth, Tom; Eisner, Manuel; Obsuth, Ingrid; Ribeaud, Denis

2018-05-22

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).

Maximum Likelihood Implementation of an Isolation-with-Migration Model for Three Species.

PubMed

Dalquen, Daniel A; Zhu, Tianqi; Yang, Ziheng

2017-05-01

We develop a maximum likelihood (ML) method for estimating migration rates between species using genomic sequence data. A species tree is used to accommodate the phylogenetic relationships among three species, allowing for migration between the two sister species, while the third species is used as an out-group. A Markov chain characterization of the genealogical process of coalescence and migration is used to integrate out the migration histories at each locus analytically, whereas Gaussian quadrature is used to integrate over the coalescent times on each genealogical tree numerically. This is an extension of our early implementation of the symmetrical isolation-with-migration model for three species to accommodate arbitrary loci with two or three sequences per locus and to allow asymmetrical migration rates. Our implementation can accommodate tens of thousands of loci, making it feasible to analyze genome-scale data sets to test for gene flow. We calculate the posterior probabilities of gene trees at individual loci to identify genomic regions that are likely to have been transferred between species due to gene flow. We conduct a simulation study to examine the statistical properties of the likelihood ratio test for gene flow between the two in-group species and of the ML estimates of model parameters such as the migration rate. Inclusion of data from a third out-group species is found to increase dramatically the power of the test and the precision of parameter estimation. We compiled and analyzed several genomic data sets from the Drosophila fruit flies. Our analyses suggest no migration from D. melanogaster to D. simulans, and a significant amount of gene flow from D. simulans to D. melanogaster, at the rate of ~0.02 migrant individuals per generation. We discuss the utility of the multispecies coalescent model for species tree estimation, accounting for incomplete lineage sorting and migration. © The Author(s) 2016. Published by Oxford University Press, on

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

Maximum likelihood estimators for truncated and censored power-law distributions show how neuronal avalanches may be misevaluated

NASA Astrophysics Data System (ADS)

Langlois, Dominic; Cousineau, Denis; Thivierge, J. P.

2014-01-01

The coordination of activity amongst populations of neurons in the brain is critical to cognition and behavior. One form of coordinated activity that has been widely studied in recent years is the so-called neuronal avalanche, whereby ongoing bursts of activity follow a power-law distribution. Avalanches that follow a power law are not unique to neuroscience, but arise in a broad range of natural systems, including earthquakes, magnetic fields, biological extinctions, fluid dynamics, and superconductors. Here, we show that common techniques that estimate this distribution fail to take into account important characteristics of the data and may lead to a sizable misestimation of the slope of power laws. We develop an alternative series of maximum likelihood estimators for discrete, continuous, bounded, and censored data. Using numerical simulations, we show that these estimators lead to accurate evaluations of power-law distributions, improving on common approaches. Next, we apply these estimators to recordings of in vitro rat neocortical activity. We show that different estimators lead to marked discrepancies in the evaluation of power-law distributions. These results call into question a broad range of findings that may misestimate the slope of power laws by failing to take into account key aspects of the observed data.

Maximum likelihood estimators for truncated and censored power-law distributions show how neuronal avalanches may be misevaluated.

PubMed

Langlois, Dominic; Cousineau, Denis; Thivierge, J P

2014-01-01

The coordination of activity amongst populations of neurons in the brain is critical to cognition and behavior. One form of coordinated activity that has been widely studied in recent years is the so-called neuronal avalanche, whereby ongoing bursts of activity follow a power-law distribution. Avalanches that follow a power law are not unique to neuroscience, but arise in a broad range of natural systems, including earthquakes, magnetic fields, biological extinctions, fluid dynamics, and superconductors. Here, we show that common techniques that estimate this distribution fail to take into account important characteristics of the data and may lead to a sizable misestimation of the slope of power laws. We develop an alternative series of maximum likelihood estimators for discrete, continuous, bounded, and censored data. Using numerical simulations, we show that these estimators lead to accurate evaluations of power-law distributions, improving on common approaches. Next, we apply these estimators to recordings of in vitro rat neocortical activity. We show that different estimators lead to marked discrepancies in the evaluation of power-law distributions. These results call into question a broad range of findings that may misestimate the slope of power laws by failing to take into account key aspects of the observed data.

Phylogenetic place of guinea pigs: no support of the rodent-polyphyly hypothesis from maximum-likelihood analyses of multiple protein sequences.

PubMed

Cao, Y; Adachi, J; Yano, T; Hasegawa, M

1994-07-01

Graur et al.'s (1991) hypothesis that the guinea pig-like rodents have an evolutionary origin within mammals that is separate from that of other rodents (the rodent-polyphyly hypothesis) was reexamined by the maximum-likelihood method for protein phylogeny, as well as by the maximum-parsimony and neighbor-joining methods. The overall evidence does not support Graur et al.'s hypothesis, which radically contradicts the traditional view of rodent monophyly. This work demonstrates that we must be careful in choosing a proper method for phylogenetic inference and that an argument based on a small data set (with respect to the length of the sequence and especially the number of species) may be unstable.

A composite likelihood approach for spatially correlated survival data

PubMed Central

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

A composite likelihood approach for spatially correlated survival data.

PubMed

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Digital tomosynthesis mammography using a parallel maximum-likelihood reconstruction method

NASA Astrophysics Data System (ADS)

Wu, Tao; Zhang, Juemin; Moore, Richard; Rafferty, Elizabeth; Kopans, Daniel; Meleis, Waleed; Kaeli, David

2004-05-01

A parallel reconstruction method, based on an iterative maximum likelihood (ML) algorithm, is developed to provide fast reconstruction for digital tomosynthesis mammography. Tomosynthesis mammography acquires 11 low-dose projections of a breast by moving an x-ray tube over a 50° angular range. In parallel reconstruction, each projection is divided into multiple segments along the chest-to-nipple direction. Using the 11 projections, segments located at the same distance from the chest wall are combined to compute a partial reconstruction of the total breast volume. The shape of the partial reconstruction forms a thin slab, angled toward the x-ray source at a projection angle 0°. The reconstruction of the total breast volume is obtained by merging the partial reconstructions. The overlap region between neighboring partial reconstructions and neighboring projection segments is utilized to compensate for the incomplete data at the boundary locations present in the partial reconstructions. A serial execution of the reconstruction is compared to a parallel implementation, using clinical data. The serial code was run on a PC with a single PentiumIV 2.2GHz CPU. The parallel implementation was developed using MPI and run on a 64-node Linux cluster using 800MHz Itanium CPUs. The serial reconstruction for a medium-sized breast (5cm thickness, 11cm chest-to-nipple distance) takes 115 minutes, while a parallel implementation takes only 3.5 minutes. The reconstruction time for a larger breast using a serial implementation takes 187 minutes, while a parallel implementation takes 6.5 minutes. No significant differences were observed between the reconstructions produced by the serial and parallel implementations.

Programmer's manual for MMLE3, a general FORTRAN program for maximum likelihood parameter estimation

NASA Technical Reports Server (NTRS)

Maine, R. E.

1981-01-01

The MMLE3 is a maximum likelihood parameter estimation program capable of handling general bilinear dynamic equations of arbitrary order with measurement noise and/or state noise (process noise). The basic MMLE3 program is quite general and, therefore, applicable to a wide variety of problems. The basic program can interact with a set of user written problem specific routines to simplify the use of the program on specific systems. A set of user routines for the aircraft stability and control derivative estimation problem is provided with the program. The implementation of the program on specific computer systems is discussed. The structure of the program is diagrammed, and the function and operation of individual routines is described. Complete listings and reference maps of the routines are included on microfiche as a supplement. Four test cases are discussed; listings of the input cards and program output for the test cases are included on microfiche as a supplement.

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

PubMed

Andersen, Tobias S

2015-05-01

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

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

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

Efficient method for computing the maximum-likelihood quantum state from measurements with additive Gaussian noise.

PubMed

Smolin, John A; Gambetta, Jay M; Smith, Graeme

2012-02-17

We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.

Maximum likelihood estimation of protein kinetic parameters under weak assumptions from unfolding force spectroscopy experiments

NASA Astrophysics Data System (ADS)

Aioanei, Daniel; Samorì, Bruno; Brucale, Marco

2009-12-01

Single molecule force spectroscopy (SMFS) is extensively used to characterize the mechanical unfolding behavior of individual protein domains under applied force by pulling chimeric polyproteins consisting of identical tandem repeats. Constant velocity unfolding SMFS data can be employed to reconstruct the protein unfolding energy landscape and kinetics. The methods applied so far require the specification of a single stretching force increase function, either theoretically derived or experimentally inferred, which must then be assumed to accurately describe the entirety of the experimental data. The very existence of a suitable optimal force model, even in the context of a single experimental data set, is still questioned. Herein, we propose a maximum likelihood (ML) framework for the estimation of protein kinetic parameters which can accommodate all the established theoretical force increase models. Our framework does not presuppose the existence of a single force characteristic function. Rather, it can be used with a heterogeneous set of functions, each describing the protein behavior in the stretching time range leading to one rupture event. We propose a simple way of constructing such a set of functions via piecewise linear approximation of the SMFS force vs time data and we prove the suitability of the approach both with synthetic data and experimentally. Additionally, when the spontaneous unfolding rate is the only unknown parameter, we find a correction factor that eliminates the bias of the ML estimator while also reducing its variance. Finally, we investigate which of several time-constrained experiment designs leads to better estimators.

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar. Thesis Technical Report No. 20

NASA Technical Reports Server (NTRS)

Lai, Jonathan Y.

1994-01-01

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

Maximum likelihood density modification by pattern recognition of structural motifs

DOEpatents

Terwilliger, Thomas C.

2004-04-13

An electron density for a crystallographic structure having protein regions and solvent regions is improved by maximizing the log likelihood of a set of structures factors {F.sub.h } using a local log-likelihood function: (x)+p(.rho.(x).vertline.SOLV)p.sub.SOLV (x)+p(.rho.(x).vertline.H)p.sub.H (x)], where p.sub.PROT (x) is the probability that x is in the protein region, p(.rho.(x).vertline.PROT) is the conditional probability for .rho.(x) given that x is in the protein region, and p.sub.SOLV (x) and p(.rho.(x).vertline.SOLV) are the corresponding quantities for the solvent region, p.sub.H (x) refers to the probability that there is a structural motif at a known location, with a known orientation, in the vicinity of the point x; and p(.rho.(x).vertline.H) is the probability distribution for electron density at this point given that the structural motif actually is present. One appropriate structural motif is a helical structure within the crystallographic structure.

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

PubMed Central

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

2012-01-01

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

Maximum likelihood estimation of linkage disequilibrium in half-sib families.

PubMed

Gomez-Raya, L

2012-05-01

Maximum likelihood methods for the estimation of linkage disequilibrium between biallelic DNA-markers in half-sib families (half-sib method) are developed for single and multifamily situations. Monte Carlo computer simulations were carried out for a variety of scenarios regarding sire genotypes, linkage disequilibrium, recombination fraction, family size, and number of families. A double heterozygote sire was simulated with recombination fraction of 0.00, linkage disequilibrium among dams of δ=0.10, and alleles at both markers segregating at intermediate frequencies for a family size of 500. The average estimates of δ were 0.17, 0.25, and 0.10 for Excoffier and Slatkin (1995), maternal informative haplotypes, and the half-sib method, respectively. A multifamily EM algorithm was tested at intermediate frequencies by computer simulation. The range of the absolute difference between estimated and simulated δ was between 0.000 and 0.008. A cattle half-sib family was genotyped with the Illumina 50K BeadChip. There were 314,730 SNP pairs for which the sire was a homo-heterozygote with average estimates of r2 of 0.115, 0.067, and 0.111 for half-sib, Excoffier and Slatkin (1995), and maternal informative haplotypes methods, respectively. There were 208,872 SNP pairs for which the sire was double heterozygote with average estimates of r2 across the genome of 0.100, 0.267, and 0.925 for half-sib, Excoffier and Slatkin (1995), and maternal informative haplotypes methods, respectively. Genome analyses for all possible sire genotypes with 829,042 tests showed that ignoring half-sib family structure leads to upward biased estimates of linkage disequilibrium. Published inferences on population structure and evolution of cattle should be revisited after accommodating existing half-sib family structure in the estimation of linkage disequilibrium.

Maximum Likelihood Estimation of Linkage Disequilibrium in Half-Sib Families

PubMed Central

Gomez-Raya, L.

2012-01-01

Maximum likelihood methods for the estimation of linkage disequilibrium between biallelic DNA-markers in half-sib families (half-sib method) are developed for single and multifamily situations. Monte Carlo computer simulations were carried out for a variety of scenarios regarding sire genotypes, linkage disequilibrium, recombination fraction, family size, and number of families. A double heterozygote sire was simulated with recombination fraction of 0.00, linkage disequilibrium among dams of δ = 0.10, and alleles at both markers segregating at intermediate frequencies for a family size of 500. The average estimates of δ were 0.17, 0.25, and 0.10 for Excoffier and Slatkin (1995), maternal informative haplotypes, and the half-sib method, respectively. A multifamily EM algorithm was tested at intermediate frequencies by computer simulation. The range of the absolute difference between estimated and simulated δ was between 0.000 and 0.008. A cattle half-sib family was genotyped with the Illumina 50K BeadChip. There were 314,730 SNP pairs for which the sire was a homo-heterozygote with average estimates of r2 of 0.115, 0.067, and 0.111 for half-sib, Excoffier and Slatkin (1995), and maternal informative haplotypes methods, respectively. There were 208,872 SNP pairs for which the sire was double heterozygote with average estimates of r2 across the genome of 0.100, 0.267, and 0.925 for half-sib, Excoffier and Slatkin (1995), and maternal informative haplotypes methods, respectively. Genome analyses for all possible sire genotypes with 829,042 tests showed that ignoring half-sib family structure leads to upward biased estimates of linkage disequilibrium. Published inferences on population structure and evolution of cattle should be revisited after accommodating existing half-sib family structure in the estimation of linkage disequilibrium. PMID:22377635

Practical aspects of a maximum likelihood estimation method to extract stability and control derivatives from flight data

NASA Technical Reports Server (NTRS)

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

1976-01-01

A maximum likelihood estimation method was applied to flight data and procedures to facilitate the routine analysis of a large amount of flight data were described. Techniques that can be used to obtain stability and control derivatives from aircraft maneuvers that are less than ideal for this purpose are described. The techniques involve detecting and correcting the effects of dependent or nearly dependent variables, structural vibration, data drift, inadequate instrumentation, and difficulties with the data acquisition system and the mathematical model. The use of uncertainty levels and multiple maneuver analysis also proved to be useful in improving the quality of the estimated coefficients. The procedures used for editing the data and for overall analysis are also discussed.

Performance advantages of maximum likelihood methods in PRBS-modulated time-of-flight electron energy loss spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Zhongyu

This thesis describes the design, experimental performance, and theoretical simulation of a novel time-of-flight analyzer that was integrated into a high resolution electron energy loss spectrometer (TOF-HREELS). First we examined the use of an interleaved comb chopper for chopping a continuous electron beam. Both static and dynamic behaviors were simulated theoretically and measured experimentally, with very good agreement. The finite penetration of the field beyond the plane of the chopper leads to non-ideal chopper response, which is characterized in terms of an "energy corruption" effect and a lead or lag in the time at which the beam responds to the chopper potential. Second we considered the recovery of spectra from pseudo-random binary sequence (PRBS) modulated TOF-HREELS data. The effects of the Poisson noise distribution and the non-ideal behavior of the "interleaved comb" chopper were simulated. We showed, for the first time, that maximum likelihood methods can be combined with PRBS modulation to achieve resolution enhancement, while properly accounting for the Poisson noise distribution and artifacts introduced by the chopper. Our results indicate that meV resolution, similar to that of modern high resolution electron energy loss spectrometers, can be achieved with a dramatic performance advantage over conventional, serial detection analyzers. To demonstrate the capabilities of the TOF-HREELS instrument, we made measurements on a highly oriented thin film polytetrafluoroethylene (PTFE) sample. We demonstrated that the TOF-HREELS can achieve a throughput advantage of a factor of 85 compared to the conventional HREELS instrument. Comparisons were made between the experimental results and theoretical simulations. We discuss various factors which affect inversion of PRBS modulated Time of Flight (TOF) data with the Lucy algorithm. Using simulations, we conclude that the convolution assumption was good under the conditions of our experiment. The chopper rise

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters

DOE PAGES

Li, Xinya; Deng, Z. Daniel; USA, Richland Washington; ...

2014-11-27

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developedmore » using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.« less

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters

NASA Astrophysics Data System (ADS)

Li, Xinya; Deng, Z. Daniel; Sun, Yannan; Martinez, Jayson J.; Fu, Tao; McMichael, Geoffrey A.; Carlson, Thomas J.

2014-11-01

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developed using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters

PubMed Central

Li, Xinya; Deng, Z. Daniel; Sun, Yannan; Martinez, Jayson J.; Fu, Tao; McMichael, Geoffrey A.; Carlson, Thomas J.

2014-01-01

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developed using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature. PMID:25427517

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters.

PubMed

Li, Xinya; Deng, Z Daniel; Sun, Yannan; Martinez, Jayson J; Fu, Tao; McMichael, Geoffrey A; Carlson, Thomas J

2014-11-27

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developed using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters

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

Li, Xinya; Deng, Z. Daniel; USA, Richland Washington

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developedmore » using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.« less

Maximum-Entropy Inference with a Programmable Annealer

PubMed Central

Chancellor, Nicholas; Szoke, Szilard; Vinci, Walter; Aeppli, Gabriel; Warburton, Paul A.

2016-01-01

Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition. PMID:26936311

A real-time signal combining system for Ka-band feed arrays using maximum-likelihood weight estimates

NASA Technical Reports Server (NTRS)

Vilnrotter, V. A.; Rodemich, E. R.

1990-01-01

A real-time digital signal combining system for use with Ka-band feed arrays is proposed. The combining system attempts to compensate for signal-to-noise ratio (SNR) loss resulting from antenna deformations induced by gravitational and atmospheric effects. The combining weights are obtained directly from the observed samples by using a sliding-window implementation of a vector maximum-likelihood parameter estimator. It is shown that with averaging times of about 0.1 second, combining loss for a seven-element array can be limited to about 0.1 dB in a realistic operational environment. This result suggests that the real-time combining system proposed here is capable of recovering virtually all of the signal power captured by the feed array, even in the presence of severe wind gusts and similar disturbances.

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

PubMed Central

2010-01-01

Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service. PMID:21034504

Rotorcraft Blade Mode Damping Identification from Random Responses Using a Recursive Maximum Likelihood Algorithm

NASA Technical Reports Server (NTRS)

Molusis, J. A.

1982-01-01

An on line technique is presented for the identification of rotor blade modal damping and frequency from rotorcraft random response test data. The identification technique is based upon a recursive maximum likelihood (RML) algorithm, which is demonstrated to have excellent convergence characteristics in the presence of random measurement noise and random excitation. The RML technique requires virtually no user interaction, provides accurate confidence bands on the parameter estimates, and can be used for continuous monitoring of modal damping during wind tunnel or flight testing. Results are presented from simulation random response data which quantify the identified parameter convergence behavior for various levels of random excitation. The data length required for acceptable parameter accuracy is shown to depend upon the amplitude of random response and the modal damping level. Random response amplitudes of 1.25 degrees to .05 degrees are investigated. The RML technique is applied to hingeless rotor test data. The inplane lag regressing mode is identified at different rotor speeds. The identification from the test data is compared with the simulation results and with other available estimates of frequency and damping.

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

DOE PAGES

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

2016-12-01

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

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

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Terrain Classification on Venus from Maximum-Likelihood Inversion of Parameterized Models of Topography, Gravity, and their Relation

NASA Astrophysics Data System (ADS)

Eggers, G. L.; Lewis, K. W.; Simons, F. J.; Olhede, S.

2013-12-01

Venus does not possess a plate-tectonic system like that observed on Earth, and many surface features--such as tesserae and coronae--lack terrestrial equivalents. To understand Venus' tectonics is to understand its lithosphere, requiring a study of topography and gravity, and how they relate. Past studies of topography dealt with mapping and classification of visually observed features, and studies of gravity dealt with inverting the relation between topography and gravity anomalies to recover surface density and elastic thickness in either the space (correlation) or the spectral (admittance, coherence) domain. In the former case, geological features could be delineated but not classified quantitatively. In the latter case, rectangular or circular data windows were used, lacking geological definition. While the estimates of lithospheric strength on this basis were quantitative, they lacked robust error estimates. Here, we remapped the surface into 77 regions visually and qualitatively defined from a combination of Magellan topography, gravity, and radar images. We parameterize the spectral covariance of the observed topography, treating it as a Gaussian process assumed to be stationary over the mapped regions, using a three-parameter isotropic Matern model, and perform maximum-likelihood based inversions for the parameters. We discuss the parameter distribution across the Venusian surface and across terrain types such as coronoae, dorsae, tesserae, and their relation with mean elevation and latitudinal position. We find that the three-parameter model, while mathematically established and applicable to Venus topography, is overparameterized, and thus reduce the results to a two-parameter description of the peak spectral variance and the range-to-half-peak variance (in function of the wavenumber). With the reduction the clustering of geological region types in two-parameter space becomes promising. Finally, we perform inversions for the JOINT spectral variance of

Maximum-likelihood-based extended-source spatial acquisition and tracking for planetary optical communications

NASA Astrophysics Data System (ADS)

Tsou, Haiping; Yan, Tsun-Yee

1999-04-01

This paper describes an extended-source spatial acquisition and tracking scheme for planetary optical communications. This scheme uses the Sun-lit Earth image as the beacon signal, which can be computed according to the current Sun-Earth-Probe angle from a pre-stored Earth image or a received snapshot taken by other Earth-orbiting satellite. Onboard the spacecraft, the reference image is correlated in the transform domain with the received image obtained from a detector array, which is assumed to have each of its pixels corrupted by an independent additive white Gaussian noise. The coordinate of the ground station is acquired and tracked, respectively, by an open-loop acquisition algorithm and a closed-loop tracking algorithm derived from the maximum likelihood criterion. As shown in the paper, the optimal spatial acquisition requires solving two nonlinear equations, or iteratively solving their linearized variants, to estimate the coordinate when translation in the relative positions of onboard and ground transceivers is considered. Similar assumption of linearization leads to the closed-loop spatial tracking algorithm in which the loop feedback signals can be derived from the weighted transform-domain correlation. Numerical results using a sample Sun-lit Earth image demonstrate that sub-pixel resolutions can be achieved by this scheme in a high disturbance environment.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

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

Brendel, Bernhard, E-mail: bernhard.brendel@philips.com; Teuffenbach, Maximilian von; Noël, Peter B.

2016-01-15

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penaltymore » comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.« less

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

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

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

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.

Closed-loop carrier phase synchronization techniques motivated by likelihood functions

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

The Construct Validity of Higher Order Structure-of-Intellect Abilities in a Battery of Tests Emphasizing the Product of Transformations: A Confirmatory Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Khattab, Ali-Maher; And Others

1982-01-01

A causal modeling system, using confirmatory maximum likelihood factor analysis with the LISREL IV computer program, evaluated the construct validity underlying the higher order factor structure of a given correlation matrix of 46 structure-of-intellect tests emphasizing the product of transformations. (Author/PN)

An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

PubMed

Galili, Tal; Meilijson, Isaac

2016-01-02

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].

Maximum likelihood identification and optimal input design for identifying aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Stepner, D. E.; Mehra, R. K.

1973-01-01

A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.

Identification of multiple leaks in pipeline: Linearized model, maximum likelihood, and super-resolution localization

NASA Astrophysics Data System (ADS)

Wang, Xun; Ghidaoui, Mohamed S.

2018-07-01

This paper considers the problem of identifying multiple leaks in a water-filled pipeline based on inverse transient wave theory. The analytical solution to this problem involves nonlinear interaction terms between the various leaks. This paper shows analytically and numerically that these nonlinear terms are of the order of the leak sizes to the power two and; thus, negligible. As a result of this simplification, a maximum likelihood (ML) scheme that identifies leak locations and leak sizes separately is formulated and tested. It is found that the ML estimation scheme is highly efficient and robust with respect to noise. In addition, the ML method is a super-resolution leak localization scheme because its resolvable leak distance (approximately 0.15λmin , where λmin is the minimum wavelength) is below the Nyquist-Shannon sampling theorem limit (0.5λmin). Moreover, the Cramér-Rao lower bound (CRLB) is derived and used to show the efficiency of the ML scheme estimates. The variance of the ML estimator approximates the CRLB proving that the ML scheme belongs to class of best unbiased estimator of leak localization methods.

Penalized maximum likelihood simultaneous longitudinal PET image reconstruction with difference-image priors.

PubMed

Ellis, Sam; Reader, Andrew J

2018-04-26

Many clinical contexts require the acquisition of multiple positron emission tomography (PET) scans of a single subject, for example, to observe and quantitate changes in functional behaviour in tumors after treatment in oncology. Typically, the datasets from each of these scans are reconstructed individually, without exploiting the similarities between them. We have recently shown that sharing information between longitudinal PET datasets by penalizing voxel-wise differences during image reconstruction can improve reconstructed images by reducing background noise and increasing the contrast-to-noise ratio of high-activity lesions. Here, we present two additional novel longitudinal difference-image priors and evaluate their performance using two-dimesional (2D) simulation studies and a three-dimensional (3D) real dataset case study. We have previously proposed a simultaneous difference-image-based penalized maximum likelihood (PML) longitudinal image reconstruction method that encourages sparse difference images (DS-PML), and in this work we propose two further novel prior terms. The priors are designed to encourage longitudinal images with corresponding differences which have (a) low entropy (DE-PML), and (b) high sparsity in their spatial gradients (DTV-PML). These two new priors and the originally proposed longitudinal prior were applied to 2D-simulated treatment response [ 18 F]fluorodeoxyglucose (FDG) brain tumor datasets and compared to standard maximum likelihood expectation-maximization (MLEM) reconstructions. These 2D simulation studies explored the effects of penalty strengths, tumor behaviour, and interscan coupling on reconstructed images. Finally, a real two-scan longitudinal data series acquired from a head and neck cancer patient was reconstructed with the proposed methods and the results compared to standard reconstruction methods. Using any of the three priors with an appropriate penalty strength produced images with noise levels equivalent to

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W., Jr.

2003-01-01

A simple power law model consisting of a single spectral index, sigma(sub 2), is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index sigma(sub 2) greater than sigma(sub 1) above E(sub k). The maximum likelihood (ML) procedure was developed for estimating the single parameter sigma(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (Pl) consistency (asymptotically unbiased), (P2) efficiency (asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only be ascertained by calculating the CRB for an assumed energy spectrum- detector response function combination, which can be quite formidable in practice. However, the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are stained in practice are investigated.

Maximum Likelihood Time-of-Arrival Estimation of Optical Pulses via Photon-Counting Photodetectors

NASA Technical Reports Server (NTRS)

Erkmen, Baris I.; Moision, Bruce E.

2010-01-01

Many optical imaging, ranging, and communications systems rely on the estimation of the arrival time of an optical pulse. Recently, such systems have been increasingly employing photon-counting photodetector technology, which changes the statistics of the observed photocurrent. This requires time-of-arrival estimators to be developed and their performances characterized. The statistics of the output of an ideal photodetector, which are well modeled as a Poisson point process, were considered. An analytical model was developed for the mean-square error of the maximum likelihood (ML) estimator, demonstrating two phenomena that cause deviations from the minimum achievable error at low signal power. An approximation was derived to the threshold at which the ML estimator essentially fails to provide better than a random guess of the pulse arrival time. Comparing the analytic model performance predictions to those obtained via simulations, it was verified that the model accurately predicts the ML performance over all regimes considered. There is little prior art that attempts to understand the fundamental limitations to time-of-arrival estimation from Poisson statistics. This work establishes both a simple mathematical description of the error behavior, and the associated physical processes that yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior.

Deconvolving the wedge: maximum-likelihood power spectra via spherical-wave visibility modelling

NASA Astrophysics Data System (ADS)

Ghosh, A.; Mertens, F. G.; Koopmans, L. V. E.

2018-03-01

Direct detection of the Epoch of Reionization (EoR) via the red-shifted 21-cm line will have unprecedented implications on the study of structure formation in the infant Universe. To fulfil this promise, current and future 21-cm experiments need to detect this weak EoR signal in the presence of foregrounds that are several orders of magnitude larger. This requires extreme noise control and improved wide-field high dynamic-range imaging techniques. We propose a new imaging method based on a maximum likelihood framework which solves for the interferometric equation directly on the sphere, or equivalently in the uvw-domain. The method uses the one-to-one relation between spherical waves and spherical harmonics (SpH). It consistently handles signals from the entire sky, and does not require a w-term correction. The SpH coefficients represent the sky-brightness distribution and the visibilities in the uvw-domain, and provide a direct estimate of the spatial power spectrum. Using these spectrally smooth SpH coefficients, bright foregrounds can be removed from the signal, including their side-lobe noise, which is one of the limiting factors in high dynamics-range wide-field imaging. Chromatic effects causing the so-called `wedge' are effectively eliminated (i.e. deconvolved) in the cylindrical (k⊥, k∥) power spectrum, compared to a power spectrum computed directly from the images of the foreground visibilities where the wedge is clearly present. We illustrate our method using simulated Low-Frequency Array observations, finding an excellent reconstruction of the input EoR signal with minimal bias.

Estimating contaminant loads in rivers: An application of adjusted maximum likelihood to type 1 censored data

USGS Publications Warehouse

Cohn, Timothy A.

2005-01-01

This paper presents an adjusted maximum likelihood estimator (AMLE) that can be used to estimate fluvial transport of contaminants, like phosphorus, that are subject to censoring because of analytical detection limits. The AMLE is a generalization of the widely accepted minimum variance unbiased estimator (MVUE), and Monte Carlo experiments confirm that it shares essentially all of the MVUE's desirable properties, including high efficiency and negligible bias. In particular, the AMLE exhibits substantially less bias than alternative censored‐data estimators such as the MLE (Tobit) or the MLE followed by a jackknife. As with the MLE and the MVUE the AMLE comes close to achieving the theoretical Frechet‐Cramér‐Rao bounds on its variance. This paper also presents a statistical framework, applicable to both censored and complete data, for understanding and estimating the components of uncertainty associated with load estimates. This can serve to lower the cost and improve the efficiency of both traditional and real‐time water quality monitoring.

Development of an LSI maximum-likelihood convolutional decoder for advanced forward error correction capability on the NASA 30/20 GHz program

NASA Technical Reports Server (NTRS)

Clark, R. T.; Mccallister, R. D.

1982-01-01

The particular coding option identified as providing the best level of coding gain performance in an LSI-efficient implementation was the optimal constraint length five, rate one-half convolutional code. To determine the specific set of design parameters which optimally matches this decoder to the LSI constraints, a breadboard MCD (maximum-likelihood convolutional decoder) was fabricated and used to generate detailed performance trade-off data. The extensive performance testing data gathered during this design tradeoff study are summarized, and the functional and physical MCD chip characteristics are presented.

Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG

PubMed Central

2014-01-01

Background We propose a mathematical model for multichannel assessment of the trial-to-trial variability of auditory evoked brain responses in magnetoencephalography (MEG). Methods Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices. Extending the work of de Munck et al., where the trial-to-trial variability of the responses was considered identical to all channels, we evaluate it for each individual channel. Results Simulations with two equivalent current dipoles (ECDs) with different trial-to-trial variability, one seeded in each of the auditory cortices, were used to study the applicability of the proposed methodology on the sensor level and revealed spatial selectivity of the trial-to-trial estimates. In addition, we simulated a scenario with neighboring ECDs, to show limitations of the method. We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation. Conclusions The proposed algorithm is capable of reconstructing lateralization effects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one hemisphere habituates, whereas the activity of the other hemisphere is not subject to habituation. Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing. PMID:24939398

Enhancing resolution and contrast in second-harmonic generation microscopy using an advanced maximum likelihood estimation restoration method

NASA Astrophysics Data System (ADS)

Sivaguru, Mayandi; Kabir, Mohammad M.; Gartia, Manas Ranjan; Biggs, David S. C.; Sivaguru, Barghav S.; Sivaguru, Vignesh A.; Berent, Zachary T.; Wagoner Johnson, Amy J.; Fried, Glenn A.; Liu, Gang Logan; Sadayappan, Sakthivel; Toussaint, Kimani C.

2017-02-01

Second-harmonic generation (SHG) microscopy is a label-free imaging technique to study collagenous materials in extracellular matrix environment with high resolution and contrast. However, like many other microscopy techniques, the actual spatial resolution achievable by SHG microscopy is reduced by out-of-focus blur and optical aberrations that degrade particularly the amplitude of the detectable higher spatial frequencies. Being a two-photon scattering process, it is challenging to define a point spread function (PSF) for the SHG imaging modality. As a result, in comparison with other two-photon imaging systems like two-photon fluorescence, it is difficult to apply any PSF-engineering techniques to enhance the experimental spatial resolution closer to the diffraction limit. Here, we present a method to improve the spatial resolution in SHG microscopy using an advanced maximum likelihood estimation (AdvMLE) algorithm to recover the otherwise degraded higher spatial frequencies in an SHG image. Through adaptation and iteration, the AdvMLE algorithm calculates an improved PSF for an SHG image and enhances the spatial resolution by decreasing the full-width-at-halfmaximum (FWHM) by 20%. Similar results are consistently observed for biological tissues with varying SHG sources, such as gold nanoparticles and collagen in porcine feet tendons. By obtaining an experimental transverse spatial resolution of 400 nm, we show that the AdvMLE algorithm brings the practical spatial resolution closer to the theoretical diffraction limit. Our approach is suitable for adaptation in micro-nano CT and MRI imaging, which has the potential to impact diagnosis and treatment of human diseases.

Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

PubMed

Xie, Yanmei; Zhang, Biao

2017-04-20

Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and

Lateral stability and control derivatives of a jet fighter airplane extracted from flight test data by utilizing maximum likelihood estimation

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Steinmetz, G. G.

1972-01-01

A method of parameter extraction for stability and control derivatives of aircraft from flight test data, implementing maximum likelihood estimation, has been developed and successfully applied to actual lateral flight test data from a modern sophisticated jet fighter. This application demonstrates the important role played by the analyst in combining engineering judgment and estimator statistics to yield meaningful results. During the analysis, the problems of uniqueness of the extracted set of parameters and of longitudinal coupling effects were encountered and resolved. The results for all flight runs are presented in tabular form and as time history comparisons between the estimated states and the actual flight test data.

Formulating the Rasch Differential Item Functioning Model under the Marginal Maximum Likelihood Estimation Context and Its Comparison with Mantel-Haenszel Procedure in Short Test and Small Sample Conditions

ERIC Educational Resources Information Center

Paek, Insu; Wilson, Mark

2011-01-01

This study elaborates the Rasch differential item functioning (DIF) model formulation under the marginal maximum likelihood estimation context. Also, the Rasch DIF model performance was examined and compared with the Mantel-Haenszel (MH) procedure in small sample and short test length conditions through simulations. The theoretically known…

Evidence of seasonal variation in longitudinal growth of height in a sample of boys from Stuttgart Carlsschule, 1771-1793, using combined principal component analysis and maximum likelihood principle.

PubMed

Lehmann, A; Scheffler, Ch; Hermanussen, M

2010-02-01

Recent progress in modelling individual growth has been achieved by combining the principal component analysis and the maximum likelihood principle. This combination models growth even in incomplete sets of data and in data obtained at irregular intervals. We re-analysed late 18th century longitudinal growth of German boys from the boarding school Carlsschule in Stuttgart. The boys, aged 6-23 years, were measured at irregular 3-12 monthly intervals during the period 1771-1793. At the age of 18 years, mean height was 1652 mm, but height variation was large. The shortest boy reached 1474 mm, the tallest 1826 mm. Measured height closely paralleled modelled height, with mean difference of 4 mm, SD 7 mm. Seasonal height variation was found. Low growth rates occurred in spring and high growth rates in summer and autumn. The present study demonstrates that combining the principal component analysis and the maximum likelihood principle enables growth modelling in historic height data also. Copyright (c) 2009 Elsevier GmbH. All rights reserved.

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

PubMed

Eberhard, Wynn L

2017-04-01

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

The evolution of autodigestion in the mushroom family Psathyrellaceae (Agaricales) inferred from Maximum Likelihood and Bayesian methods.

PubMed

Nagy, László G; Urban, Alexander; Orstadius, Leif; Papp, Tamás; Larsson, Ellen; Vágvölgyi, Csaba

2010-12-01

Recently developed comparative phylogenetic methods offer a wide spectrum of applications in evolutionary biology, although it is generally accepted that their statistical properties are incompletely known. Here, we examine and compare the statistical power of the ML and Bayesian methods with regard to selection of best-fit models of fruiting-body evolution and hypothesis testing of ancestral states on a real-life data set of a physiological trait (autodigestion) in the family Psathyrellaceae. Our phylogenies are based on the first multigene data set generated for the family. Two different coding regimes (binary and multistate) and two data sets differing in taxon sampling density are examined. The Bayesian method outperformed Maximum Likelihood with regard to statistical power in all analyses. This is particularly evident if the signal in the data is weak, i.e. in cases when the ML approach does not provide support to choose among competing hypotheses. Results based on binary and multistate coding differed only modestly, although it was evident that multistate analyses were less conclusive in all cases. It seems that increased taxon sampling density has favourable effects on inference of ancestral states, while model parameters are influenced to a smaller extent. The model best fitting our data implies that the rate of losses of deliquescence equals zero, although model selection in ML does not provide proper support to reject three of the four candidate models. The results also support the hypothesis that non-deliquescence (lack of autodigestion) has been ancestral in Psathyrellaceae, and that deliquescent fruiting bodies represent the preferred state, having evolved independently several times during evolution. Copyright © 2010 Elsevier Inc. All rights reserved.

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

PubMed

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Search for Point Sources of Ultra-High-Energy Cosmic Rays above 4.0 × 1019 eV Using a Maximum Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Abbasi, R. U.; Abu-Zayyad, T.; Amann, J. F.; Archbold, G.; Atkins, R.; Bellido, J. A.; Belov, K.; Belz, J. W.; Ben-Zvi, S. Y.; Bergman, D. R.; Boyer, J. H.; Burt, G. W.; Cao, Z.; Clay, R. W.; Connolly, B. M.; Dawson, B. R.; Deng, W.; Farrar, G. R.; Fedorova, Y.; Findlay, J.; Finley, C. B.; Hanlon, W. F.; Hoffman, C. M.; Holzscheiter, M. H.; Hughes, G. A.; Hüntemeyer, P.; Jui, C. C. H.; Kim, K.; Kirn, M. A.; Knapp, B. C.; Loh, E. C.; Maestas, M. M.; Manago, N.; Mannel, E. J.; Marek, L. J.; Martens, K.; Matthews, J. A. J.; Matthews, J. N.; O'Neill, A.; Painter, C. A.; Perera, L.; Reil, K.; Riehle, R.; Roberts, M. D.; Sasaki, M.; Schnetzer, S. R.; Seman, M.; Simpson, K. M.; Sinnis, G.; Smith, J. D.; Snow, R.; Sokolsky, P.; Song, C.; Springer, R. W.; Stokes, B. T.; Thomas, J. R.; Thomas, S. B.; Thomson, G. B.; Tupa, D.; Westerhoff, S.; Wiencke, L. R.; Zech, A.

2005-04-01

We present the results of a search for cosmic-ray point sources at energies in excess of 4.0×1019 eV in the combined data sets recorded by the Akeno Giant Air Shower Array and High Resolution Fly's Eye stereo experiments. The analysis is based on a maximum likelihood ratio test using the probability density function for each event rather than requiring an a priori choice of a fixed angular bin size. No statistically significant clustering of events consistent with a point source is found.

Methods for flexible sample-size design in clinical trials: Likelihood, weighted, dual test, and promising zone approaches.

PubMed

Shih, Weichung Joe; Li, Gang; Wang, Yining

2016-03-01

Sample size plays a crucial role in clinical trials. Flexible sample-size designs, as part of the more general category of adaptive designs that utilize interim data, have been a popular topic in recent years. In this paper, we give a comparative review of four related methods for such a design. The likelihood method uses the likelihood ratio test with an adjusted critical value. The weighted method adjusts the test statistic with given weights rather than the critical value. The dual test method requires both the likelihood ratio statistic and the weighted statistic to be greater than the unadjusted critical value. The promising zone approach uses the likelihood ratio statistic with the unadjusted value and other constraints. All four methods preserve the type-I error rate. In this paper we explore their properties and compare their relationships and merits. We show that the sample size rules for the dual test are in conflict with the rules of the promising zone approach. We delineate what is necessary to specify in the study protocol to ensure the validity of the statistical procedure and what can be kept implicit in the protocol so that more flexibility can be attained for confirmatory phase III trials in meeting regulatory requirements. We also prove that under mild conditions, the likelihood ratio test still preserves the type-I error rate when the actual sample size is larger than the re-calculated one. Copyright © 2015 Elsevier Inc. All rights reserved.

Modeling and Maximum Likelihood Fitting of Gamma-Ray and Radio Light Curves of Millisecond Pulsars Detected with Fermi

NASA Technical Reports Server (NTRS)

Johnson, T. J.; Harding, A. K.; Venter, C.

2012-01-01

Pulsed gamma rays have been detected with the Fermi Large Area Telescope (LAT) from more than 20 millisecond pulsars (MSPs), some of which were discovered in radio observations of bright, unassociated LAT sources. We have fit the radio and gamma-ray light curves of 19 LAT-detected MSPs in the context of geometric, outermagnetospheric emission models assuming the retarded vacuum dipole magnetic field using a Markov chain Monte Carlo maximum likelihood technique. We find that, in many cases, the models are able to reproduce the observed light curves well and provide constraints on the viewing geometries that are in agreement with those from radio polarization measurements. Additionally, for some MSPs we constrain the altitudes of both the gamma-ray and radio emission regions. The best-fit magnetic inclination angles are found to cover a broader range than those of non-recycled gamma-ray pulsars.

Craniofacial form and function in Metriorhynchidae (Crocodylomorpha: Thalattosuchia): modelling phenotypic evolution with maximum-likelihood methods.

PubMed

Young, Mark T; Bell, Mark A; Brusatte, Stephen L

2011-12-23

Metriorhynchid crocodylomorphs were the only group of archosaurs to fully adapt to a pelagic lifestyle. During the Jurassic and Early Cretaceous, this group diversified into a variety of ecological and morphological types, from large super-predators with a broad short snout and serrated teeth to specialized piscivores/teuthophages with an elongate tubular snout and uncarinated teeth. Here, we use an integrated repertoire of geometric morphometric (form), biomechanical finite-element analysis (FEA; function) and phylogenetic data to examine the nature of craniofacial evolution in this clade. FEA stress values significantly correlate with morphometric values representing skull length and breadth, indicating that form and function are associated. Maximum-likelihood methods, which assess which of several models of evolution best explain the distribution of form and function data on a phylogenetic tree, show that the two major metriorhynchid subclades underwent different evolutionary modes. In geosaurines, both form and function are best explained as evolving under 'random' Brownian motion, whereas in metriorhynchines, the form metrics are best explained as evolving under stasis and the function metric as undergoing a directional change (towards most efficient low-stress piscivory). This suggests that the two subclades were under different selection pressures, and that metriorhynchines with similar skull shape were driven to become functionally divergent.

Approximate likelihood approaches for detecting the influence of primordial gravitational waves in cosmic microwave background polarization

NASA Astrophysics Data System (ADS)

Pan, Zhen; Anderes, Ethan; Knox, Lloyd

2018-05-01

One of the major targets for next-generation cosmic microwave background (CMB) experiments is the detection of the primordial B-mode signal. Planning is under way for Stage-IV experiments that are projected to have instrumental noise small enough to make lensing and foregrounds the dominant source of uncertainty for estimating the tensor-to-scalar ratio r from polarization maps. This makes delensing a crucial part of future CMB polarization science. In this paper we present a likelihood method for estimating the tensor-to-scalar ratio r from CMB polarization observations, which combines the benefits of a full-scale likelihood approach with the tractability of the quadratic delensing technique. This method is a pixel space, all order likelihood analysis of the quadratic delensed B modes, and it essentially builds upon the quadratic delenser by taking into account all order lensing and pixel space anomalies. Its tractability relies on a crucial factorization of the pixel space covariance matrix of the polarization observations which allows one to compute the full Gaussian approximate likelihood profile, as a function of r , at the same computational cost of a single likelihood evaluation.

Empirical likelihood method for non-ignorable missing data problems.

PubMed

Guan, Zhong; Qin, Jing

2017-01-01

Missing response problem is ubiquitous in survey sampling, medical, social science and epidemiology studies. It is well known that non-ignorable missing is the most difficult missing data problem where the missing of a response depends on its own value. In statistical literature, unlike the ignorable missing data problem, not many papers on non-ignorable missing data are available except for the full parametric model based approach. In this paper we study a semiparametric model for non-ignorable missing data in which the missing probability is known up to some parameters, but the underlying distributions are not specified. By employing Owen (1988)'s empirical likelihood method we can obtain the constrained maximum empirical likelihood estimators of the parameters in the missing probability and the mean response which are shown to be asymptotically normal. Moreover the likelihood ratio statistic can be used to test whether the missing of the responses is non-ignorable or completely at random. The theoretical results are confirmed by a simulation study. As an illustration, the analysis of a real AIDS trial data shows that the missing of CD4 counts around two years are non-ignorable and the sample mean based on observed data only is biased.

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history

EPA Science Inventory

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for ...

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W.

2002-01-01

A simple power law model consisting of a single spectral index, a is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The Maximum likelihood (ML) procedure was developed for estimating the single parameter alpha(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (P1) consistency (asymptotically unbiased). (P2) efficiency asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only he ascertained by calculating the CRB for an assumed energy spectrum-detector response function combination, which can be quite formidable in practice. However. the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are attained in practice are investigated. The ML technique is then extended to estimate spectra information from

Improving Depth, Energy and Timing Estimation in PET Detectors with Deconvolution and Maximum Likelihood Pulse Shape Discrimination

PubMed Central

Berg, Eric; Roncali, Emilie; Hutchcroft, Will; Qi, Jinyi; Cherry, Simon R.

2016-01-01

In a scintillation detector, the light generated in the scintillator by a gamma interaction is converted to photoelectrons by a photodetector and produces a time-dependent waveform, the shape of which depends on the scintillator properties and the photodetector response. Several depth-of-interaction (DOI) encoding strategies have been developed that manipulate the scintillator’s temporal response along the crystal length and therefore require pulse shape discrimination techniques to differentiate waveform shapes. In this work, we demonstrate how maximum likelihood (ML) estimation methods can be applied to pulse shape discrimination to better estimate deposited energy, DOI and interaction time (for time-of-flight (TOF) PET) of a gamma ray in a scintillation detector. We developed likelihood models based on either the estimated detection times of individual photoelectrons or the number of photoelectrons in discrete time bins, and applied to two phosphor-coated crystals (LFS and LYSO) used in a previously developed TOF-DOI detector concept. Compared with conventional analytical methods, ML pulse shape discrimination improved DOI encoding by 27% for both crystals. Using the ML DOI estimate, we were able to counter depth-dependent changes in light collection inherent to long scintillator crystals and recover the energy resolution measured with fixed depth irradiation (~11.5% for both crystals). Lastly, we demonstrated how the Richardson-Lucy algorithm, an iterative, ML-based deconvolution technique, can be applied to the digitized waveforms to deconvolve the photodetector’s single photoelectron response and produce waveforms with a faster rising edge. After deconvolution and applying DOI and time-walk corrections, we demonstrated a 13% improvement in coincidence timing resolution (from 290 to 254 ps) with the LFS crystal and an 8% improvement (323 to 297 ps) with the LYSO crystal. PMID:27295658

Improving Depth, Energy and Timing Estimation in PET Detectors with Deconvolution and Maximum Likelihood Pulse Shape Discrimination.

PubMed

Berg, Eric; Roncali, Emilie; Hutchcroft, Will; Qi, Jinyi; Cherry, Simon R

2016-11-01

In a scintillation detector, the light generated in the scintillator by a gamma interaction is converted to photoelectrons by a photodetector and produces a time-dependent waveform, the shape of which depends on the scintillator properties and the photodetector response. Several depth-of-interaction (DOI) encoding strategies have been developed that manipulate the scintillator's temporal response along the crystal length and therefore require pulse shape discrimination techniques to differentiate waveform shapes. In this work, we demonstrate how maximum likelihood (ML) estimation methods can be applied to pulse shape discrimination to better estimate deposited energy, DOI and interaction time (for time-of-flight (TOF) PET) of a gamma ray in a scintillation detector. We developed likelihood models based on either the estimated detection times of individual photoelectrons or the number of photoelectrons in discrete time bins, and applied to two phosphor-coated crystals (LFS and LYSO) used in a previously developed TOF-DOI detector concept. Compared with conventional analytical methods, ML pulse shape discrimination improved DOI encoding by 27% for both crystals. Using the ML DOI estimate, we were able to counter depth-dependent changes in light collection inherent to long scintillator crystals and recover the energy resolution measured with fixed depth irradiation (~11.5% for both crystals). Lastly, we demonstrated how the Richardson-Lucy algorithm, an iterative, ML-based deconvolution technique, can be applied to the digitized waveforms to deconvolve the photodetector's single photoelectron response and produce waveforms with a faster rising edge. After deconvolution and applying DOI and time-walk corrections, we demonstrated a 13% improvement in coincidence timing resolution (from 290 to 254 ps) with the LFS crystal and an 8% improvement (323 to 297 ps) with the LYSO crystal.

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

PubMed

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

2012-02-01

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

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Efficient Maximum Likelihood Estimation for Pedigree Data with the Sum-Product Algorithm.

PubMed

Engelhardt, Alexander; Rieger, Anna; Tresch, Achim; Mansmann, Ulrich

2016-01-01

We analyze data sets consisting of pedigrees with age at onset of colorectal cancer (CRC) as phenotype. The occurrence of familial clusters of CRC suggests the existence of a latent, inheritable risk factor. We aimed to compute the probability of a family possessing this risk factor as well as the hazard rate increase for these risk factor carriers. Due to the inheritability of this risk factor, the estimation necessitates a costly marginalization of the likelihood. We propose an improved EM algorithm by applying factor graphs and the sum-product algorithm in the E-step. This reduces the computational complexity from exponential to linear in the number of family members. Our algorithm is as precise as a direct likelihood maximization in a simulation study and a real family study on CRC risk. For 250 simulated families of size 19 and 21, the runtime of our algorithm is faster by a factor of 4 and 29, respectively. On the largest family (23 members) in the real data, our algorithm is 6 times faster. We introduce a flexible and runtime-efficient tool for statistical inference in biomedical event data with latent variables that opens the door for advanced analyses of pedigree data. © 2017 S. Karger AG, Basel.

Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial.

PubMed

Balzer, Laura; Staples, Patrick; Onnela, Jukka-Pekka; DeGruttola, Victor

2017-04-01

Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV- persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies. We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV- individuals who self-identify as high risk, (2) all HIV- individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV- individuals, and (4) the HIV- contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male-female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis. The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger

Likelihood-based modification of experimental crystal structure electron density maps

DOEpatents

Terwilliger, Thomas C [Sante Fe, NM

2005-04-16

A maximum-likelihood method for improves an electron density map of an experimental crystal structure. A likelihood of a set of structure factors {F.sub.h } is formed for the experimental crystal structure as (1) the likelihood of having obtained an observed set of structure factors {F.sub.h.sup.OBS } if structure factor set {F.sub.h } was correct, and (2) the likelihood that an electron density map resulting from {F.sub.h } is consistent with selected prior knowledge about the experimental crystal structure. The set of structure factors {F.sub.h } is then adjusted to maximize the likelihood of {F.sub.h } for the experimental crystal structure. An improved electron density map is constructed with the maximized structure factors.

A real-time digital program for estimating aircraft stability and control parameters from flight test data by using the maximum likelihood method

NASA Technical Reports Server (NTRS)

Grove, R. D.; Mayhew, S. C.

1973-01-01

A computer program (Langley program C1123) has been developed for estimating aircraft stability and control parameters from flight test data. These parameters are estimated by the maximum likelihood estimation procedure implemented on a real-time digital simulation system, which uses the Control Data 6600 computer. This system allows the investigator to interact with the program in order to obtain satisfactory results. Part of this system, the control and display capabilities, is described for this program. This report also describes the computer program by presenting the program variables, subroutines, flow charts, listings, and operational features. Program usage is demonstrated with a test case using pseudo or simulated flight data.

An Adjusted Likelihood Ratio Approach Analysing Distribution of Food Products to Assist the Investigation of Foodborne Outbreaks

PubMed Central

Norström, Madelaine; Kristoffersen, Anja Bråthen; Görlach, Franziska Sophie; Nygård, Karin; Hopp, Petter

2015-01-01

In order to facilitate foodborne outbreak investigations there is a need to improve the methods for identifying the food products that should be sampled for laboratory analysis. The aim of this study was to examine the applicability of a likelihood ratio approach previously developed on simulated data, to real outbreak data. We used human case and food product distribution data from the Norwegian enterohaemorrhagic Escherichia coli outbreak in 2006. The approach was adjusted to include time, space smoothing and to handle missing or misclassified information. The performance of the adjusted likelihood ratio approach on the data originating from the HUS outbreak and control data indicates that the adjusted approach is promising and indicates that the adjusted approach could be a useful tool to assist and facilitate the investigation of food borne outbreaks in the future if good traceability are available and implemented in the distribution chain. However, the approach needs to be further validated on other outbreak data and also including other food products than meat products in order to make a more general conclusion of the applicability of the developed approach. PMID:26237468

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

ERIC Educational Resources Information Center

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

1998-01-01

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

Univariate and bivariate likelihood-based meta-analysis methods performed comparably when marginal sensitivity and specificity were the targets of inference.

PubMed

Dahabreh, Issa J; Trikalinos, Thomas A; Lau, Joseph; Schmid, Christopher H

2017-03-01

To compare statistical methods for meta-analysis of sensitivity and specificity of medical tests (e.g., diagnostic or screening tests). We constructed a database of PubMed-indexed meta-analyses of test performance from which 2 × 2 tables for each included study could be extracted. We reanalyzed the data using univariate and bivariate random effects models fit with inverse variance and maximum likelihood methods. Analyses were performed using both normal and binomial likelihoods to describe within-study variability. The bivariate model using the binomial likelihood was also fit using a fully Bayesian approach. We use two worked examples-thoracic computerized tomography to detect aortic injury and rapid prescreening of Papanicolaou smears to detect cytological abnormalities-to highlight that different meta-analysis approaches can produce different results. We also present results from reanalysis of 308 meta-analyses of sensitivity and specificity. Models using the normal approximation produced sensitivity and specificity estimates closer to 50% and smaller standard errors compared to models using the binomial likelihood; absolute differences of 5% or greater were observed in 12% and 5% of meta-analyses for sensitivity and specificity, respectively. Results from univariate and bivariate random effects models were similar, regardless of estimation method. Maximum likelihood and Bayesian methods produced almost identical summary estimates under the bivariate model; however, Bayesian analyses indicated greater uncertainty around those estimates. Bivariate models produced imprecise estimates of the between-study correlation of sensitivity and specificity. Differences between methods were larger with increasing proportion of studies that were small or required a continuity correction. The binomial likelihood should be used to model within-study variability. Univariate and bivariate models give similar estimates of the marginal distributions for sensitivity and

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

USGS Publications Warehouse

Langbein, John O.

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

Langbein, John

2017-08-01

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

Predicting bulk permeability using outcrop fracture attributes: The benefits of a Maximum Likelihood Estimator

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; De Siena, L.

2015-12-01

The success of any model prediction is largely dependent on the accuracy with which its parameters are known. In characterising fracture networks in naturally fractured rocks, the main issues are related with the difficulties in accurately up- and down-scaling the parameters governing the distribution of fracture attributes. Optimal characterisation and analysis of fracture attributes (fracture lengths, apertures, orientations and densities) represents a fundamental step which can aid the estimation of permeability and fluid flow, which are of primary importance in a number of contexts ranging from hydrocarbon production in fractured reservoirs and reservoir stimulation by hydrofracturing, to geothermal energy extraction and deeper Earth systems, such as earthquakes and ocean floor hydrothermal venting. This work focuses on linking fracture data collected directly from outcrops to permeability estimation and fracture network modelling. Outcrop studies can supplement the limited data inherent to natural fractured systems in the subsurface. The study area is a highly fractured upper Miocene biosiliceous mudstone formation cropping out along the coastline north of Santa Cruz (California, USA). These unique outcrops exposes a recently active bitumen-bearing formation representing a geological analogue of a fractured top seal. In order to validate field observations as useful analogues of subsurface reservoirs, we describe a methodology of statistical analysis for more accurate probability distribution of fracture attributes, using Maximum Likelihood Estimators. These procedures aim to understand whether the average permeability of a fracture network can be predicted reducing its uncertainties, and if outcrop measurements of fracture attributes can be used directly to generate statistically identical fracture network models.

Efficient computation of the phylogenetic likelihood function on multi-gene alignments and multi-core architectures.

PubMed

Stamatakis, Alexandros; Ott, Michael

2008-12-27

The continuous accumulation of sequence data, for example, due to novel wet-laboratory techniques such as pyrosequencing, coupled with the increasing popularity of multi-gene phylogenies and emerging multi-core processor architectures that face problems of cache congestion, poses new challenges with respect to the efficient computation of the phylogenetic maximum-likelihood (ML) function. Here, we propose two approaches that can significantly speed up likelihood computations that typically represent over 95 per cent of the computational effort conducted by current ML or Bayesian inference programs. Initially, we present a method and an appropriate data structure to efficiently compute the likelihood score on 'gappy' multi-gene alignments. By 'gappy' we denote sampling-induced gaps owing to missing sequences in individual genes (partitions), i.e. not real alignment gaps. A first proof-of-concept implementation in RAXML indicates that this approach can accelerate inferences on large and gappy alignments by approximately one order of magnitude. Moreover, we present insights and initial performance results on multi-core architectures obtained during the transition from an OpenMP-based to a Pthreads-based fine-grained parallelization of the ML function.

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources

PubMed Central

Chatterjee, Nilanjan; Chen, Yi-Hau; Maas, Paige; Carroll, Raymond J.

2016-01-01

Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for utilizing information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an “internal” study while utilizing summary-level information, such as information on parameters for reduced models, from an “external” big data source. We identify a set of very general constraints that link internal and external models. These constraints are used to develop a framework for semiparametric maximum likelihood inference that allows the distribution of covariates to be estimated using either the internal sample or an external reference sample. We develop extensions for handling complex stratified sampling designs, such as case-control sampling, for the internal study. Asymptotic theory and variance estimators are developed for each case. We use simulation studies and a real data application to assess the performance of the proposed methods in contrast to the generalized regression (GR) calibration methodology that is popular in the sample survey literature. PMID:27570323

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

NASA Astrophysics Data System (ADS)

Hernandez, Marcel

2017-12-01

In this paper, a novel target acquisition and localisation algorithm (TALA) is introduced that offers a capability for detecting and localising multiple targets using the intermittent "signals-of-opportunity" (e.g. acoustic impulses or radio frequency transmissions) they generate. The TALA is a batch estimator that addresses the complex multi-sensor/multi-target data association problem in order to estimate the locations of an unknown number of targets. The TALA is unique in that it does not require measurements to be of a specific type, and can be implemented for systems composed of either homogeneous or heterogeneous sensors. The performance of the TALA is demonstrated in simulated scenarios with a network of 20 sensors and up to 10 targets. The sensors generate angle-of-arrival (AOA), time-of-arrival (TOA), or hybrid AOA/TOA measurements. It is shown that the TALA is able to successfully detect 83-99% of the targets, with a negligible number of false targets declared. Furthermore, the localisation errors of the TALA are typically within 10% of the errors generated by a "genie" algorithm that is given the correct measurement-to-target associations. The TALA also performs well in comparison with an optimistic Cramér-Rao lower bound, with typical differences in performance of 10-20%, and differences in performance of 40-50% in the most difficult scenarios considered. The computational expense of the TALA is also controllable, which allows the TALA to maintain computational feasibility even in the most challenging scenarios considered. This allows the approach to be implemented in time-critical scenarios, such as in the localisation of artillery firing events. It is concluded that the TALA provides a powerful situational awareness aid for passive surveillance operations.

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory. [Project Psychometric Aspects of Item Banking No. 53.] Research Report 91-1.

ERIC Educational Resources Information Center

Kelderman, Henk

In this paper, algorithms are described for obtaining the maximum likelihood estimates of the parameters in log-linear models. Modified versions of the iterative proportional fitting and Newton-Raphson algorithms are described that work on the minimal sufficient statistics rather than on the usual counts in the full contingency table. This is…

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated

DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood.

PubMed

Kim, Tane; Hao, Weilong

2014-09-27

The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired. DiscML performs maximum likelihood estimation for evolutionary rates of discrete characters on a provided phylogeny with the options that correct for unobservable data, rate variations, and unknown prior root probabilities from the empirical data. It gives users options to customize the instantaneous transition rate matrices, or to choose pre-determined matrices from models such as birth-and-death (BD), birth-death-and-innovation (BDI), equal rates (ER), symmetric (SYM), general time-reversible (GTR) and all rates different (ARD). Moreover, we show application examples of DiscML on gene family data and on intron presence/absence data. DiscML was developed as a unified R program for estimating evolutionary rates of discrete characters with no restriction on the number of character states, and with flexibility to use different transition models. DiscML is ideal for the analyses of binary (1s/0s) patterns, multi-gene families, and multistate discrete morphological characteristics.

SU-C-207A-01: A Novel Maximum Likelihood Method for High-Resolution Proton Radiography/proton CT

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

Collins-Fekete, C; Centre Hospitalier University de Quebec, Quebec, QC; Mass General Hospital

2016-06-15

Purpose: Multiple Coulomb scattering is the largest contributor to blurring in proton imaging. Here we tested a maximum likelihood least squares estimator (MLLSE) to improve the spatial resolution of proton radiography (pRad) and proton computed tomography (pCT). Methods: The object is discretized into voxels and the average relative stopping power through voxel columns defined from the source to the detector pixels is optimized such that it maximizes the likelihood of the proton energy loss. The length spent by individual protons in each column is calculated through an optimized cubic spline estimate. pRad images were first produced using Geant4 simulations. Anmore » anthropomorphic head phantom and the Catphan line-pair module for 3-D spatial resolution were studied and resulting images were analyzed. Both parallel and conical beam have been investigated for simulated pRad acquisition. Then, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Specific filters were applied on proton angle and energy loss data to remove proton histories that underwent nuclear interactions. The MTF10% (lp/mm) was used to evaluate and compare spatial resolution. Results: Numerical simulations showed improvement in the pRad spatial resolution for the parallel (2.75 to 6.71 lp/cm) and conical beam (3.08 to 5.83 lp/cm) reconstructed with the MLLSE compared to averaging detector pixel signals. For full tomographic reconstruction, the improved pRad were used as input into a simultaneous algebraic reconstruction algorithm. The Catphan pCT reconstruction based on the MLLSE-enhanced projection showed spatial resolution improvement for the parallel (2.83 to 5.86 lp/cm) and conical beam (3.03 to 5.15 lp/cm). The anthropomorphic head pCT displayed important contrast gains in high-gradient regions. Experimental results also demonstrated significant improvement in spatial resolution of the pediatric head radiography. Conclusion

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

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

2018-05-07

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

Maximum-likelihood curve-fitting scheme for experiments with pulsed lasers subject to intensity fluctuations.

PubMed

Metz, Thomas; Walewski, Joachim; Kaminski, Clemens F

2003-03-20

Evaluation schemes, e.g., least-squares fitting, are not generally applicable to any types of experiments. If the evaluation schemes were not derived from a measurement model that properly described the experiment to be evaluated, poorer precision or accuracy than attainable from the measured data could result. We outline ways in which statistical data evaluation schemes should be derived for all types of experiment, and we demonstrate them for laser-spectroscopic experiments, in which pulse-to-pulse fluctuations of the laser power cause correlated variations of laser intensity and generated signal intensity. The method of maximum likelihood is demonstrated in the derivation of an appropriate fitting scheme for this type of experiment. Statistical data evaluation contains the following steps. First, one has to provide a measurement model that considers statistical variation of all enclosed variables. Second, an evaluation scheme applicable to this particular model has to be derived or provided. Third, the scheme has to be characterized in terms of accuracy and precision. A criterion for accepting an evaluation scheme is that it have accuracy and precision as close as possible to the theoretical limit. The fitting scheme derived for experiments with pulsed lasers is compared to well-established schemes in terms of fitting power and rational functions. The precision is found to be as much as three timesbetter than for simple least-squares fitting. Our scheme also suppresses the bias on the estimated model parameters that other methods may exhibit if they are applied in an uncritical fashion. We focus on experiments in nonlinear spectroscopy, but the fitting scheme derived is applicable in many scientific disciplines.

Likelihood Ratios for the Emergency Physician.

PubMed

Peng, Paul; Coyle, Andrew

2018-04-26

The concept of likelihood ratios was introduced more than 40 years ago, yet this powerful metric has still not seen wider application or discussion in the medical decision-making process. There is concern that clinicians-in-training are still being taught an over-simplified approach to diagnostic test performance, and have limited exposure to likelihood ratios. Even for those familiar with likelihood ratios, they might perceive them as mathematically-cumbersome in application, if not difficult to determine for a particular disease process. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Beyond Roughness: Maximum-Likelihood Estimation of Topographic "Structure" on Venus and Elsewhere in the Solar System

NASA Astrophysics Data System (ADS)

Simons, F. J.; Eggers, G. L.; Lewis, K. W.; Olhede, S. C.

2015-12-01

What numbers "capture" topography? If stationary, white, and Gaussian: mean and variance. But "whiteness" is strong; we are led to a "baseline" over which to compute means and variances. We then have subscribed to topography as a correlated process, and to the estimation (noisy, afftected by edge effects) of the parameters of a spatial or spectral covariance function. What if the covariance function or the point process itself aren't Gaussian? What if the region under study isn't regularly shaped or sampled? How can results from differently sized patches be compared robustly? We present a spectral-domain "Whittle" maximum-likelihood procedure that circumvents these difficulties and answers the above questions. The key is the Matern form, whose parameters (variance, range, differentiability) define the shape of the covariance function (Gaussian, exponential, ..., are all special cases). We treat edge effects in simulation and in estimation. Data tapering allows for the irregular regions. We determine the estimation variance of all parameters. And the "best" estimate may not be "good enough": we test whether the "model" itself warrants rejection. We illustrate our methodology on geologically mapped patches of Venus. Surprisingly few numbers capture planetary topography. We derive them, with uncertainty bounds, we simulate "new" realizations of patches that look to the geologists exactly as if they were derived from similar processes. Our approach holds in 1, 2, and 3 spatial dimensions, and generalizes to multiple variables, e.g. when topography and gravity are being considered jointly (perhaps linked by flexural rigidity, erosion, or other surface and sub-surface modifying processes). Our results have widespread implications for the study of planetary topography in the Solar System, and are interpreted in the light of trying to derive "process" from "parameters", the end goal to assign likely formation histories for the patches under consideration. Our results

Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT

NASA Astrophysics Data System (ADS)

Makeev, Andrey; Ikejimba, Lynda; Lo, Joseph Y.; Glick, Stephen J.

2016-03-01

Although digital mammography has reduced breast cancer mortality by approximately 30%, sensitivity and specificity are still far from perfect. In particular, the performance of mammography is especially limited for women with dense breast tissue. Two out of every three biopsies performed in the U.S. are unnecessary, thereby resulting in increased patient anxiety, pain, and possible complications. One promising tomographic breast imaging method that has recently been approved by the FDA is dedicated breast computed tomography (BCT). However, visualizing lesions with BCT can still be challenging for women with dense breast tissue due to the minimal contrast for lesions surrounded by fibroglandular tissue. In recent years there has been renewed interest in improving lesion conspicuity in x-ray breast imaging by administration of an iodinated contrast agent. Due to the fully 3-D imaging nature of BCT, as well as sub-optimal contrast enhancement while the breast is under compression with mammography and breast tomosynthesis, dedicated BCT of the uncompressed breast is likely to offer the best solution for injected contrast-enhanced x-ray breast imaging. It is well known that use of statistically-based iterative reconstruction in CT results in improved image quality at lower radiation dose. Here we investigate possible improvements in image reconstruction for BCT, by optimizing free regularization parameter in method of maximum likelihood and comparing its performance with clinical cone-beam filtered backprojection (FBP) algorithm.

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

PubMed

Martyna, Agnieszka; Zadora, Grzegorz; Neocleous, Tereza; Michalska, Aleksandra; Dean, Nema

2016-08-10

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. Copyright © 2016 Elsevier B.V. All rights reserved.

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.

PubMed

Berglund, Lars; Garmo, Hans; Lindbäck, Johan; Svärdsudd, Kurt; Zethelius, Björn

2008-09-30

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations. Copyright (c) 2008 John Wiley & Sons, Ltd.

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

PubMed Central

Schwab, Joshua; Gruber, Susan; Blaser, Nello; Schomaker, Michael; van der Laan, Mark

2015-01-01

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the

Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

USGS Publications Warehouse

Curtis, Gary P.; Lu, Dan; Ye, Ming

2015-01-01

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the

Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

DOE PAGES

Lu, Dan; Ye, Ming; Curtis, Gary P.

2015-08-01

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict themore » reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally

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

NASA Technical Reports Server (NTRS)

Pierson, W. J.

1982-01-01

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

Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

PubMed Central

Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

2016-01-01

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. PMID:27322267

Maximum likelihood estimation of between and within variations in energy and protein intakes from infancy to adolescence for the Philippines.

PubMed

Bhargava, A; Bouis, H

1992-02-28

The assessment of subjects' 'usual' intake of nutrients is important in assessing relationships between diet and disease and in identifying malnourished sub-groups of the populations. Estimation of the variation in intakes within subjects over time ('within variation') has importance in epidemiologic research; estimation of the between subject variation in the sample has use in defining the recommended dietary allowances that take into account the inter-individual differences. This paper estimates the between and within variances in the energy and protein intakes of 1189 Filipino children, based on 4 rounds of 24-hour recall data within a dynamic framework by means of maximum likelihood. The main findings are that the proportion of variation due to the within variance is higher for children from poorer households. Also, from the estimates of dynamic regression models for nutrient intakes of children and adults, it appears that school programmes that provide subsidized foods with good sources of protein to the poorest among school attendees will be cost effective.

Analysis of an all-digital maximum likelihood carrier phase and clock timing synchronizer for eight phase-shift keying modulation

NASA Astrophysics Data System (ADS)

Degaudenzi, Riccardo; Vanghi, Vieri

1994-02-01

In all-digital Trellis-Coded 8PSK (TC-8PSK) demodulator well suited for VLSI implementation, including maximum likelihood estimation decision-directed (MLE-DD) carrier phase and clock timing recovery, is introduced and analyzed. By simply removing the trellis decoder the demodulator can efficiently cope with uncoded 8PSK signals. The proposed MLE-DD synchronization algorithm requires one sample for the phase and two samples per symbol for the timing loop. The joint phase and timing discriminator characteristics are analytically derived and numerical results checked by means of computer simulations. An approximated expression for steady-state carrier phase and clock timing mean square error has been derived and successfully checked with simulation findings. Synchronizer deviation from the Cramer Rao bound is also discussed. Mean acquisition time for the digital synchronizer has also been computed and checked, using the Monte Carlo simulation technique. Finally, TC-8PSK digital demodulator performance in terms of bit error rate and mean time to lose lock, including digital interpolators and synchronization loops, is presented.

Maximum likelihood positioning and energy correction for scintillation detectors

NASA Astrophysics Data System (ADS)

Lerche, Christoph W.; Salomon, André; Goldschmidt, Benjamin; Lodomez, Sarah; Weissler, Björn; Solf, Torsten

2016-02-01

An algorithm for determining the crystal pixel and the gamma ray energy with scintillation detectors for PET is presented. The algorithm uses Likelihood Maximisation (ML) and therefore is inherently robust to missing data caused by defect or paralysed photo detector pixels. We tested the algorithm on a highly integrated MRI compatible small animal PET insert. The scintillation detector blocks of the PET gantry were built with the newly developed digital Silicon Photomultiplier (SiPM) technology from Philips Digital Photon Counting and LYSO pixel arrays with a pitch of 1 mm and length of 12 mm. Light sharing was used to readout the scintillation light from the 30× 30 scintillator pixel array with an 8× 8 SiPM array. For the performance evaluation of the proposed algorithm, we measured the scanner’s spatial resolution, energy resolution, singles and prompt count rate performance, and image noise. These values were compared to corresponding values obtained with Center of Gravity (CoG) based positioning methods for different scintillation light trigger thresholds and also for different energy windows. While all positioning algorithms showed similar spatial resolution, a clear advantage for the ML method was observed when comparing the PET scanner’s overall single and prompt detection efficiency, image noise, and energy resolution to the CoG based methods. Further, ML positioning reduces the dependence of image quality on scanner configuration parameters and was the only method that allowed achieving highest energy resolution, count rate performance and spatial resolution at the same time.

A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems

NASA Astrophysics Data System (ADS)

Peng, Juan-juan; Wang, Jian-qiang; Yang, Wu-E.

2017-01-01

In this paper, multi-criteria decision-making (MCDM) problems based on the qualitative flexible multiple criteria method (QUALIFLEX), in which the criteria values are expressed by multi-valued neutrosophic information, are investigated. First, multi-valued neutrosophic sets (MVNSs), which allow the truth-membership function, indeterminacy-membership function and falsity-membership function to have a set of crisp values between zero and one, are introduced. Then the likelihood of multi-valued neutrosophic number (MVNN) preference relations is defined and the corresponding properties are also discussed. Finally, an extended QUALIFLEX approach based on likelihood is explored to solve MCDM problems where the assessments of alternatives are in the form of MVNNs; furthermore an example is provided to illustrate the application of the proposed method, together with a comparison analysis.

Sub-200 ps CRT in monolithic scintillator PET detectors using digital SiPM arrays and maximum likelihood interaction time estimation.

PubMed

van Dam, Herman T; Borghi, Giacomo; Seifert, Stefan; Schaart, Dennis R

2013-05-21

Digital silicon photomultiplier (dSiPM) arrays have favorable characteristics for application in monolithic scintillator detectors for time-of-flight positron emission tomography (PET). To fully exploit these benefits, a maximum likelihood interaction time estimation (MLITE) method was developed to derive the time of interaction from the multiple time stamps obtained per scintillation event. MLITE was compared to several deterministic methods. Timing measurements were performed with monolithic scintillator detectors based on novel dSiPM arrays and LSO:Ce,0.2%Ca crystals of 16 × 16 × 10 mm(3), 16 × 16 × 20 mm(3), 24 × 24 × 10 mm(3), and 24 × 24 × 20 mm(3). The best coincidence resolving times (CRTs) for pairs of identical detectors were obtained with MLITE and measured 157 ps, 185 ps, 161 ps, and 184 ps full-width-at-half-maximum (FWHM), respectively. For comparison, a small reference detector, consisting of a 3 × 3 × 5 mm(3) LSO:Ce,0.2%Ca crystal coupled to a single pixel of a dSiPM array, was measured to have a CRT as low as 120 ps FWHM. The results of this work indicate that the influence of the optical transport of the scintillation photons on the timing performance of monolithic scintillator detectors can at least partially be corrected for by utilizing the information contained in the spatio-temporal distribution of the collection of time stamps registered per scintillation event.

Sub-200 ps CRT in monolithic scintillator PET detectors using digital SiPM arrays and maximum likelihood interaction time estimation

NASA Astrophysics Data System (ADS)

van Dam, Herman T.; Borghi, Giacomo; Seifert, Stefan; Schaart, Dennis R.

2013-05-01

Digital silicon photomultiplier (dSiPM) arrays have favorable characteristics for application in monolithic scintillator detectors for time-of-flight positron emission tomography (PET). To fully exploit these benefits, a maximum likelihood interaction time estimation (MLITE) method was developed to derive the time of interaction from the multiple time stamps obtained per scintillation event. MLITE was compared to several deterministic methods. Timing measurements were performed with monolithic scintillator detectors based on novel dSiPM arrays and LSO:Ce,0.2%Ca crystals of 16 × 16 × 10 mm3, 16 × 16 × 20 mm3, 24 × 24 × 10 mm3, and 24 × 24 × 20 mm3. The best coincidence resolving times (CRTs) for pairs of identical detectors were obtained with MLITE and measured 157 ps, 185 ps, 161 ps, and 184 ps full-width-at-half-maximum (FWHM), respectively. For comparison, a small reference detector, consisting of a 3 × 3 × 5 mm3 LSO:Ce,0.2%Ca crystal coupled to a single pixel of a dSiPM array, was measured to have a CRT as low as 120 ps FWHM. The results of this work indicate that the influence of the optical transport of the scintillation photons on the timing performance of monolithic scintillator detectors can at least partially be corrected for by utilizing the information contained in the spatio-temporal distribution of the collection of time stamps registered per scintillation event.

Likelihood of Tree Topologies with Fossils and Diversification Rate Estimation.

PubMed

Didier, Gilles; Fau, Marine; Laurin, Michel

2017-11-01

Since the diversification process cannot be directly observed at the human scale, it has to be studied from the information available, namely the extant taxa and the fossil record. In this sense, phylogenetic trees including both extant taxa and fossils are the most complete representations of the diversification process that one can get. Such phylogenetic trees can be reconstructed from molecular and morphological data, to some extent. Among the temporal information of such phylogenetic trees, fossil ages are by far the most precisely known (divergence times are inferences calibrated mostly with fossils). We propose here a method to compute the likelihood of a phylogenetic tree with fossils in which the only considered time information is the fossil ages, and apply it to the estimation of the diversification rates from such data. Since it is required in our computation, we provide a method for determining the probability of a tree topology under the standard diversification model. Testing our approach on simulated data shows that the maximum likelihood rate estimates from the phylogenetic tree topology and the fossil dates are almost as accurate as those obtained by taking into account all the data, including the divergence times. Moreover, they are substantially more accurate than the estimates obtained only from the exact divergence times (without taking into account the fossil record). We also provide an empirical example composed of 50 Permo-Carboniferous eupelycosaur (early synapsid) taxa ranging in age from about 315 Ma (Late Carboniferous) to 270 Ma (shortly after the end of the Early Permian). Our analyses suggest a speciation (cladogenesis, or birth) rate of about 0.1 per lineage and per myr, a marginally lower extinction rate, and a considerable hidden paleobiodiversity of early synapsids. [Extinction rate; fossil ages; maximum likelihood estimation; speciation rate.]. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society

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

PubMed Central

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

2015-01-01

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

Modelling maximum river flow by using Bayesian Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Cheong, R. Y.; Gabda, D.

2017-09-01

Analysis of flood trends is vital since flooding threatens human living in terms of financial, environment and security. The data of annual maximum river flows in Sabah were fitted into generalized extreme value (GEV) distribution. Maximum likelihood estimator (MLE) raised naturally when working with GEV distribution. However, previous researches showed that MLE provide unstable results especially in small sample size. In this study, we used different Bayesian Markov Chain Monte Carlo (MCMC) based on Metropolis-Hastings algorithm to estimate GEV parameters. Bayesian MCMC method is a statistical inference which studies the parameter estimation by using posterior distribution based on Bayes’ theorem. Metropolis-Hastings algorithm is used to overcome the high dimensional state space faced in Monte Carlo method. This approach also considers more uncertainty in parameter estimation which then presents a better prediction on maximum river flow in Sabah.

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history.

PubMed

Lee, E Henry; Wickham, Charlotte; Beedlow, Peter A; Waschmann, Ronald S; Tingey, David T

2017-10-01

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for climate and forest disturbances (i.e., pests, diseases, fire). The statistical method is illustrated with a tree-ring width time series for a mature closed-canopy Douglas-fir stand on the west slopes of the Cascade Mountains of Oregon, USA that is impacted by Swiss needle cast disease caused by the foliar fungus, Phaecryptopus gaeumannii (Rhode) Petrak. The likelihood-based TSIA method is proposed for the field of dendrochronology to understand the interaction of temperature, water, and forest disturbances that are important in forest ecology and climate change studies.

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics.

PubMed

Helaers, Raphaël; Milinkovitch, Michel C

2010-07-15

The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities. Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers. The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these algorithms. MetaPIGA v2.0 gives access both to high

An FPGA-Based Real-Time Maximum Likelihood 3D Position Estimation for a Continuous Crystal PET Detector

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Xiao, Yong; Cheng, Xinyi; Li, Deng; Wang, Liwei

2016-02-01

For the continuous crystal-based positron emission tomography (PET) detector built in our lab, a maximum likelihood algorithm adapted for implementation on a field programmable gate array (FPGA) is proposed to estimate the three-dimensional (3D) coordinate of interaction position with the single-end detected scintillation light response. The row-sum and column-sum readout scheme organizes the 64 channels of photomultiplier (PMT) into eight row signals and eight column signals to be readout for X- and Y-coordinates estimation independently. By the reference events irradiated in a known oblique angle, the probability density function (PDF) for each depth-of-interaction (DOI) segment is generated, by which the reference events in perpendicular irradiation are assigned to DOI segments for generating the PDFs for X and Y estimation in each DOI layer. Evaluated by the experimental data, the algorithm achieves an average X resolution of 1.69 mm along the central X-axis, and DOI resolution of 3.70 mm over the whole thickness (0-10 mm) of crystal. The performance improvements from 2D estimation to the 3D algorithm are also presented. Benefiting from abundant resources of FPGA and a hierarchical storage arrangement, the whole algorithm can be implemented into a middle-scale FPGA. By a parallel structure in pipelines, the 3D position estimator on the FPGA can achieve a processing throughput of 15 M events/s, which is sufficient for the requirement of real-time PET imaging.

Speech processing using conditional observable maximum likelihood continuity mapping

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

Hogden, John; Nix, David

A computer implemented method enables the recognition of speech and speech characteristics. Parameters are initialized of first probability density functions that map between the symbols in the vocabulary of one or more sequences of speech codes that represent speech sounds and a continuity map. Parameters are also initialized of second probability density functions that map between the elements in the vocabulary of one or more desired sequences of speech transcription symbols and the continuity map. The parameters of the probability density functions are then trained to maximize the probabilities of the desired sequences of speech-transcription symbols. A new sequence ofmore » speech codes is then input to the continuity map having the trained first and second probability function parameters. A smooth path is identified on the continuity map that has the maximum probability for the new sequence of speech codes. The probability of each speech transcription symbol for each input speech code can then be output.« less

Safe semi-supervised learning based on weighted likelihood.

PubMed

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

Effect of formal and informal likelihood functions on uncertainty assessment in a single event rainfall-runoff model

NASA Astrophysics Data System (ADS)

Nourali, Mahrouz; Ghahraman, Bijan; Pourreza-Bilondi, Mohsen; Davary, Kamran

2016-09-01

In the present study, DREAM(ZS), Differential Evolution Adaptive Metropolis combined with both formal and informal likelihood functions, is used to investigate uncertainty of parameters of the HEC-HMS model in Tamar watershed, Golestan province, Iran. In order to assess the uncertainty of 24 parameters used in HMS, three flood events were used to calibrate and one flood event was used to validate the posterior distributions. Moreover, performance of seven different likelihood functions (L1-L7) was assessed by means of DREAM(ZS)approach. Four likelihood functions, L1-L4, Nash-Sutcliffe (NS) efficiency, Normalized absolute error (NAE), Index of agreement (IOA), and Chiew-McMahon efficiency (CM), is considered as informal, whereas remaining (L5-L7) is represented in formal category. L5 focuses on the relationship between the traditional least squares fitting and the Bayesian inference, and L6, is a hetereoscedastic maximum likelihood error (HMLE) estimator. Finally, in likelihood function L7, serial dependence of residual errors is accounted using a first-order autoregressive (AR) model of the residuals. According to the results, sensitivities of the parameters strongly depend on the likelihood function, and vary for different likelihood functions. Most of the parameters were better defined by formal likelihood functions L5 and L7 and showed a high sensitivity to model performance. Posterior cumulative distributions corresponding to the informal likelihood functions L1, L2, L3, L4 and the formal likelihood function L6 are approximately the same for most of the sub-basins, and these likelihood functions depict almost a similar effect on sensitivity of parameters. 95% total prediction uncertainty bounds bracketed most of the observed data. Considering all the statistical indicators and criteria of uncertainty assessment, including RMSE, KGE, NS, P-factor and R-factor, results showed that DREAM(ZS) algorithm performed better under formal likelihood functions L5 and L7

Comparison of candidate solar array maximum power utilization approaches. [for spacecraft propulsion

NASA Technical Reports Server (NTRS)

Costogue, E. N.; Lindena, S.

1976-01-01

A study was made of five potential approaches that can be utilized to detect the maximum power point of a solar array while sustaining operations at or near maximum power and without endangering stability or causing array voltage collapse. The approaches studied included: (1) dynamic impedance comparator, (2) reference array measurement, (3) onset of solar array voltage collapse detection, (4) parallel tracker, and (5) direct measurement. The study analyzed the feasibility and adaptability of these approaches to a future solar electric propulsion (SEP) mission, and, specifically, to a comet rendezvous mission. Such missions presented the most challenging requirements to a spacecraft power subsystem in terms of power management over large solar intensity ranges of 1.0 to 3.5 AU. The dynamic impedance approach was found to have the highest figure of merit, and the reference array approach followed closely behind. The results are applicable to terrestrial solar power systems as well as to other than SEP space missions.

MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics

PubMed Central

2010-01-01

Background The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities. Results Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers. Conclusions The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these algorithms. MetaPIGA v2

Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia

PubMed Central

Huang, Chiung-Yu; Qin, Jing

2013-01-01

The Canadian Study of Health and Aging (CSHA) employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. The prevalent cohort sampling scheme favors individuals who survive longer. Thus, the observed survival times are subject to length bias. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. This article considers semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Moreover, the asymptotic properties of the semiparametric maximum likelihood estimator are not well-studied. Motivated by the composite likelihood method (Besag 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software. When applied to the CSHA data, the proposed method estimates a significant difference in survival between the vascular dementia group and the possible Alzheimer’s disease group, while the partial likelihood method for left-truncated and right-censored data yields a greater standard error and a 95% confidence interval covering 0, thus highlighting the practical value of employing a more efficient methodology. To check the assumption of stable disease for the CSHA data, we also present new graphical and numerical tests in the article. The R code used to obtain the maximum composite partial

A quantum framework for likelihood ratios

NASA Astrophysics Data System (ADS)

Bond, Rachael L.; He, Yang-Hui; Ormerod, Thomas C.

The ability to calculate precise likelihood ratios is fundamental to science, from Quantum Information Theory through to Quantum State Estimation. However, there is no assumption-free statistical methodology to achieve this. For instance, in the absence of data relating to covariate overlap, the widely used Bayes’ theorem either defaults to the marginal probability driven “naive Bayes’ classifier”, or requires the use of compensatory expectation-maximization techniques. This paper takes an information-theoretic approach in developing a new statistical formula for the calculation of likelihood ratios based on the principles of quantum entanglement, and demonstrates that Bayes’ theorem is a special case of a more general quantum mechanical expression.

Empirical likelihood-based tests for stochastic ordering

PubMed Central

BARMI, HAMMOU EL; MCKEAGUE, IAN W.

2013-01-01

This paper develops an empirical likelihood approach to testing for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution. The proposed test statistic is formed by integrating a localized empirical likelihood statistic with respect to the empirical distribution of the pooled sample. The asymptotic null distribution of this test statistic is found to have a simple distribution-free representation in terms of standard Brownian bridge processes. The approach is used to compare the lengths of rule of Roman Emperors over various historical periods, including the “decline and fall” phase of the empire. In a simulation study, the power of the proposed test is found to improve substantially upon that of a competing test due to El Barmi and Mukerjee. PMID:23874142

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

PubMed Central

Boker, Steven M.; Brick, Timothy R.; Pritikin, Joshua N.; Wang, Yang; von Oertzen, Timo; Brown, Donald; Lach, John; Estabrook, Ryne; Hunter, Michael D.; Maes, Hermine H.; Neale, Michael C.

2015-01-01

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly-impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participants’ personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies. PMID:26717128

Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation.

PubMed

Herrera, Ronald; Berger, Ursula; von Ehrenstein, Ondine S; Díaz, Iván; Huber, Stella; Moraga Muñoz, Daniel; Radon, Katja

2017-12-27

In a town located in a desert area of Northern Chile, gold and copper open-pit mining is carried out involving explosive processes. These processes are associated with increased dust exposure, which might affect children's respiratory health. Therefore, we aimed to quantify the causal attributable risk of living close to the mines on asthma or allergic rhinoconjunctivitis risk burden in children. Data on the prevalence of respiratory diseases and potential confounders were available from a cross-sectional survey carried out in 2009 among 288 (response: 69 % ) children living in the community. The proximity of the children's home addresses to the local gold and copper mine was calculated using geographical positioning systems. We applied targeted maximum likelihood estimation to obtain the causal attributable risk (CAR) for asthma, rhinoconjunctivitis and both outcomes combined. Children living more than the first quartile away from the mines were used as the unexposed group. Based on the estimated CAR, a hypothetical intervention in which all children lived at least one quartile away from the copper mine would decrease the risk of rhinoconjunctivitis by 4.7 percentage points (CAR: - 4.7 ; 95 % confidence interval ( 95 % CI): - 8.4 ; - 0.11 ); and 4.2 percentage points (CAR: - 4.2 ; 95 % CI: - 7.9 ; - 0.05 ) for both outcomes combined. Overall, our results suggest that a hypothetical intervention intended to increase the distance between the place of residence of the highest exposed children would reduce the prevalence of respiratory disease in the community by around four percentage points. This approach could help local policymakers in the development of efficient public health strategies.

Likelihood ratio meta-analysis: New motivation and approach for an old method.

PubMed

Dormuth, Colin R; Filion, Kristian B; Platt, Robert W

2016-03-01

A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience. Copyright © 2016 Elsevier Inc. All rights reserved.

Maximum likelihood methods for investigating reporting rates of rings on hunter-shot birds

USGS Publications Warehouse

Conroy, M.J.; Morgan, B.J.T.; North, P.M.

1985-01-01

It is well known that hunters do not report 100% of the rings that they find on shot birds. Reward studies can be used to estimate what this reporting rate is, by comparison of recoveries of rings offering a monetary reward, to ordinary rings. A reward study of American Black Ducks (Anas rubripes) is used to illustrate the design, and to motivate the development of statistical models for estimation and for testing hypotheses of temporal and geographic variation in reporting rates. The method involves indexing the data (recoveries) and parameters (reporting, harvest, and solicitation rates) by geographic and temporal strata. Estimates are obtained under unconstrained (e.g., allowing temporal variability in reporting rates) and constrained (e.g., constant reporting rates) models, and hypotheses are tested by likelihood ratio. A FORTRAN program, available from the author, is used to perform the computations.

Likelihood-based confidence intervals for estimating floods with given return periods

NASA Astrophysics Data System (ADS)

Martins, Eduardo Sávio P. R.; Clarke, Robin T.

1993-06-01

This paper discusses aspects of the calculation of likelihood-based confidence intervals for T-year floods, with particular reference to (1) the two-parameter gamma distribution; (2) the Gumbel distribution; (3) the two-parameter log-normal distribution, and other distributions related to the normal by Box-Cox transformations. Calculation of the confidence limits is straightforward using the Nelder-Mead algorithm with a constraint incorporated, although care is necessary to ensure convergence either of the Nelder-Mead algorithm, or of the Newton-Raphson calculation of maximum-likelihood estimates. Methods are illustrated using records from 18 gauging stations in the basin of the River Itajai-Acu, State of Santa Catarina, southern Brazil. A small and restricted simulation compared likelihood-based confidence limits with those given by use of the central limit theorem; for the same confidence probability, the confidence limits of the simulation were wider than those of the central limit theorem, which failed more frequently to contain the true quantile being estimated. The paper discusses possible applications of likelihood-based confidence intervals in other areas of hydrological analysis.

Generalizing Terwilliger's likelihood approach: a new score statistic to test for genetic association.

PubMed

el Galta, Rachid; Uitte de Willige, Shirley; de Visser, Marieke C H; Helmer, Quinta; Hsu, Li; Houwing-Duistermaat, Jeanine J

2007-09-24

In this paper, we propose a one degree of freedom test for association between a candidate gene and a binary trait. This method is a generalization of Terwilliger's likelihood ratio statistic and is especially powerful for the situation of one associated haplotype. As an alternative to the likelihood ratio statistic, we derive a score statistic, which has a tractable expression. For haplotype analysis, we assume that phase is known. By means of a simulation study, we compare the performance of the score statistic to Pearson's chi-square statistic and the likelihood ratio statistic proposed by Terwilliger. We illustrate the method on three candidate genes studied in the Leiden Thrombophilia Study. We conclude that the statistic follows a chi square distribution under the null hypothesis and that the score statistic is more powerful than Terwilliger's likelihood ratio statistic when the associated haplotype has frequency between 0.1 and 0.4 and has a small impact on the studied disorder. With regard to Pearson's chi-square statistic, the score statistic has more power when the associated haplotype has frequency above 0.2 and the number of variants is above five.

Approach trajectory planning system for maximum concealment

NASA Technical Reports Server (NTRS)

Warner, David N., Jr.

1986-01-01

A computer-simulation study was undertaken to investigate a maximum concealment guidance technique (pop-up maneuver), which military aircraft may use to capture a glide path from masked, low-altitude flight typical of terrain following/terrain avoidance flight enroute. The guidance system applied to this problem is the Fuel Conservative Guidance System. Previous studies using this system have concentrated on the saving of fuel in basically conventional land and ship-based operations. Because this system is based on energy-management concepts, it also has direct application to the pop-up approach which exploits aircraft performance. Although the algorithm was initially designed to reduce fuel consumption, the commanded deceleration is at its upper limit during the pop-up and, therefore, is a good approximation of a minimum-time solution. Using the model of a powered-lift aircraft, the results of the study demonstrated that guidance commands generated by the system are well within the capability of an automatic flight-control system. Results for several initial approach conditions are presented.

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

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

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

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

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

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

Raghunathan, Srinivasan; Patil, Sanjaykumar; Bianchini, Federico

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

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

DOE PAGES

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

2017-08-25

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

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis.

PubMed

Nowakowska, Marzena

2017-04-01

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measurement of absolute concentrations of individual compounds in metabolite mixtures by gradient-selective time-zero 1H-13C HSQC with two concentration references and fast maximum likelihood reconstruction analysis.

PubMed

Hu, Kaifeng; Ellinger, James J; Chylla, Roger A; Markley, John L

2011-12-15

Time-zero 2D (13)C HSQC (HSQC(0)) spectroscopy offers advantages over traditional 2D NMR for quantitative analysis of solutions containing a mixture of compounds because the signal intensities are directly proportional to the concentrations of the constituents. The HSQC(0) spectrum is derived from a series of spectra collected with increasing repetition times within the basic HSQC block by extrapolating the repetition time to zero. Here we present an alternative approach to data collection, gradient-selective time-zero (1)H-(13)C HSQC(0) in combination with fast maximum likelihood reconstruction (FMLR) data analysis and the use of two concentration references for absolute concentration determination. Gradient-selective data acquisition results in cleaner spectra, and NMR data can be acquired in both constant-time and non-constant-time mode. Semiautomatic data analysis is supported by the FMLR approach, which is used to deconvolute the spectra and extract peak volumes. The peak volumes obtained from this analysis are converted to absolute concentrations by reference to the peak volumes of two internal reference compounds of known concentration: DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at the low concentration limit (which also serves as chemical shift reference) and MES (2-(N-morpholino)ethanesulfonic acid) at the high concentration limit. The linear relationship between peak volumes and concentration is better defined with two references than with one, and the measured absolute concentrations of individual compounds in the mixture are more accurate. We compare results from semiautomated gsHSQC(0) with those obtained by the original manual phase-cycled HSQC(0) approach. The new approach is suitable for automatic metabolite profiling by simultaneous quantification of multiple metabolites in a complex mixture.

Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators.

PubMed

Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang

2017-05-01

The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE 1 and MLE 2 , respectively), and Greenwood approximation (MLE gw ) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE 1 , the MLE 2 and MLE gw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE 2 and MLE gw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE 2 and MLE gw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Conditional maximum-entropy method for selecting prior distributions in Bayesian statistics

NASA Astrophysics Data System (ADS)

Abe, Sumiyoshi

2014-11-01

The conditional maximum-entropy method (abbreviated here as C-MaxEnt) is formulated for selecting prior probability distributions in Bayesian statistics for parameter estimation. This method is inspired by a statistical-mechanical approach to systems governed by dynamics with largely separated time scales and is based on three key concepts: conjugate pairs of variables, dimensionless integration measures with coarse-graining factors and partial maximization of the joint entropy. The method enables one to calculate a prior purely from a likelihood in a simple way. It is shown, in particular, how it not only yields Jeffreys's rules but also reveals new structures hidden behind them.

Branch length estimation and divergence dating: estimates of error in Bayesian and maximum likelihood frameworks.

PubMed

Schwartz, Rachel S; Mueller, Rachel L

2010-01-11

Estimates of divergence dates between species improve our understanding of processes ranging from nucleotide substitution to speciation. Such estimates are frequently based on molecular genetic differences between species; therefore, they rely on accurate estimates of the number of such differences (i.e. substitutions per site, measured as branch length on phylogenies). We used simulations to determine the effects of dataset size, branch length heterogeneity, branch depth, and analytical framework on branch length estimation across a range of branch lengths. We then reanalyzed an empirical dataset for plethodontid salamanders to determine how inaccurate branch length estimation can affect estimates of divergence dates. The accuracy of branch length estimation varied with branch length, dataset size (both number of taxa and sites), branch length heterogeneity, branch depth, dataset complexity, and analytical framework. For simple phylogenies analyzed in a Bayesian framework, branches were increasingly underestimated as branch length increased; in a maximum likelihood framework, longer branch lengths were somewhat overestimated. Longer datasets improved estimates in both frameworks; however, when the number of taxa was increased, estimation accuracy for deeper branches was less than for tip branches. Increasing the complexity of the dataset produced more misestimated branches in a Bayesian framework; however, in an ML framework, more branches were estimated more accurately. Using ML branch length estimates to re-estimate plethodontid salamander divergence dates generally resulted in an increase in the estimated age of older nodes and a decrease in the estimated age of younger nodes. Branch lengths are misestimated in both statistical frameworks for simulations of simple datasets. However, for complex datasets, length estimates are quite accurate in ML (even for short datasets), whereas few branches are estimated accurately in a Bayesian framework. Our reanalysis of

Evaluation of dynamic row-action maximum likelihood algorithm reconstruction for quantitative 15O brain PET.

PubMed

Ibaraki, Masanobu; Sato, Kaoru; Mizuta, Tetsuro; Kitamura, Keishi; Miura, Shuichi; Sugawara, Shigeki; Shinohara, Yuki; Kinoshita, Toshibumi

2009-09-01

A modified version of row-action maximum likelihood algorithm (RAMLA) using a 'subset-dependent' relaxation parameter for noise suppression, or dynamic RAMLA (DRAMA), has been proposed. The aim of this study was to assess the capability of DRAMA reconstruction for quantitative (15)O brain positron emission tomography (PET). Seventeen healthy volunteers were studied using a 3D PET scanner. The PET study included 3 sequential PET scans for C(15)O, (15)O(2) and H (2) (15) O. First, the number of main iterations (N (it)) in DRAMA was optimized in relation to image convergence and statistical image noise. To estimate the statistical variance of reconstructed images on a pixel-by-pixel basis, a sinogram bootstrap method was applied using list-mode PET data. Once the optimal N (it) was determined, statistical image noise and quantitative parameters, i.e., cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral metabolic rate of oxygen (CMRO(2)) and oxygen extraction fraction (OEF) were compared between DRAMA and conventional FBP. DRAMA images were post-filtered so that their spatial resolutions were matched with FBP images with a 6-mm FWHM Gaussian filter. Based on the count recovery data, N (it) = 3 was determined as an optimal parameter for (15)O PET data. The sinogram bootstrap analysis revealed that DRAMA reconstruction resulted in less statistical noise, especially in a low-activity region compared to FBP. Agreement of quantitative values between FBP and DRAMA was excellent. For DRAMA images, average gray matter values of CBF, CBV, CMRO(2) and OEF were 46.1 +/- 4.5 (mL/100 mL/min), 3.35 +/- 0.40 (mL/100 mL), 3.42 +/- 0.35 (mL/100 mL/min) and 42.1 +/- 3.8 (%), respectively. These values were comparable to corresponding values with FBP images: 46.6 +/- 4.6 (mL/100 mL/min), 3.34 +/- 0.39 (mL/100 mL), 3.48 +/- 0.34 (mL/100 mL/min) and 42.4 +/- 3.8 (%), respectively. DRAMA reconstruction is applicable to quantitative (15)O PET study and is superior to

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation

PubMed Central

Herrera, Ronald; Berger, Ursula; von Ehrenstein, Ondine S.; Díaz, Iván; Huber, Stella; Moraga Muñoz, Daniel; Radon, Katja

2017-01-01

In a town located in a desert area of Northern Chile, gold and copper open-pit mining is carried out involving explosive processes. These processes are associated with increased dust exposure, which might affect children’s respiratory health. Therefore, we aimed to quantify the causal attributable risk of living close to the mines on asthma or allergic rhinoconjunctivitis risk burden in children. Data on the prevalence of respiratory diseases and potential confounders were available from a cross-sectional survey carried out in 2009 among 288 (response: 69%) children living in the community. The proximity of the children’s home addresses to the local gold and copper mine was calculated using geographical positioning systems. We applied targeted maximum likelihood estimation to obtain the causal attributable risk (CAR) for asthma, rhinoconjunctivitis and both outcomes combined. Children living more than the first quartile away from the mines were used as the unexposed group. Based on the estimated CAR, a hypothetical intervention in which all children lived at least one quartile away from the copper mine would decrease the risk of rhinoconjunctivitis by 4.7 percentage points (CAR: −4.7; 95% confidence interval (95% CI): −8.4; −0.11); and 4.2 percentage points (CAR: −4.2; 95% CI: −7.9;−0.05) for both outcomes combined. Overall, our results suggest that a hypothetical intervention intended to increase the distance between the place of residence of the highest exposed children would reduce the prevalence of respiratory disease in the community by around four percentage points. This approach could help local policymakers in the development of efficient public health strategies. PMID:29280971

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

DOE PAGES

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

2014-10-16

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

PubMed Central

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

2014-01-01

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies.

PubMed

Zitzmann, Steffen; Lüdtke, Oliver; Robitzsch, Alexander

2015-01-01

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.

Probabilistic properties of the date of maximum river flow, an approach based on circular statistics in lowland, highland and mountainous catchment

NASA Astrophysics Data System (ADS)

Rutkowska, Agnieszka; Kohnová, Silvia; Banasik, Kazimierz

2018-04-01

Probabilistic properties of dates of winter, summer and annual maximum flows were studied using circular statistics in three catchments differing in topographic conditions; a lowland, highland and mountainous catchment. The circular measures of location and dispersion were used in the long-term samples of dates of maxima. The mixture of von Mises distributions was assumed as the theoretical distribution function of the date of winter, summer and annual maximum flow. The number of components was selected on the basis of the corrected Akaike Information Criterion and the parameters were estimated by means of the Maximum Likelihood method. The goodness of fit was assessed using both the correlation between quantiles and a version of the Kuiper's and Watson's test. Results show that the number of components varied between catchments and it was different for seasonal and annual maxima. Differences between catchments in circular characteristics were explained using climatic factors such as precipitation and temperature. Further studies may include circular grouping catchments based on similarity between distribution functions and the linkage between dates of maximum precipitation and maximum flow.

Modeling short duration extreme precipitation patterns using copula and generalized maximum pseudo-likelihood estimation with censoring

NASA Astrophysics Data System (ADS)

Bargaoui, Zoubeida Kebaili; Bardossy, Andràs

2015-10-01

The paper aims to develop researches on the spatial variability of heavy rainfall events estimation using spatial copula analysis. To demonstrate the methodology, short time resolution rainfall time series from Stuttgart region are analyzed. They are constituted by rainfall observations on continuous 30 min time scale recorded over a network composed by 17 raingages for the period July 1989-July 2004. The analysis is performed aggregating the observations from 30 min up to 24 h. Two parametric bivariate extreme copula models, the Husler-Reiss model and the Gumbel model are investigated. Both involve a single parameter to be estimated. Thus, model fitting is operated for every pair of stations for a giving time resolution. A rainfall threshold value representing a fixed rainfall quantile is adopted for model inference. Generalized maximum pseudo-likelihood estimation is adopted with censoring by analogy with methods of univariate estimation combining historical and paleoflood information with systematic data. Only pairs of observations greater than the threshold are assumed as systematic data. Using the estimated copula parameter, a synthetic copula field is randomly generated and helps evaluating model adequacy which is achieved using Kolmogorov Smirnov distance test. In order to assess dependence or independence in the upper tail, the extremal coefficient which characterises the tail of the joint bivariate distribution is adopted. Hence, the extremal coefficient is reported as a function of the interdistance between stations. If it is less than 1.7, stations are interpreted as dependent in the extremes. The analysis of the fitted extremal coefficients with respect to stations inter distance highlights two regimes with different dependence structures: a short spatial extent regime linked to short duration intervals (from 30 min to 6 h) with an extent of about 8 km and a large spatial extent regime related to longer rainfall intervals (from 12 h to 24 h) with an

A unified partial likelihood approach for X-chromosome association on time-to-event outcomes.

PubMed

Xu, Wei; Hao, Meiling

2018-02-01

The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E), and skewed X-chromosome inactivation (XCI-S). Although these expressions are included in various predesigned genetic variation chip platforms, the X-chromosome has generally been excluded from the majority of genome-wide association studies analyses; this is most likely due to the lack of a standardized method in handling X-chromosomal genotype data. To analyze the X-linked genetic association for time-to-event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi-square distributed can be used to assess the X-chromosome genetic association. Furthermore, if the X-chromosome expression pertains to the XCI-S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population-level model and a more general subject-level model have been developed to model the XCI-S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome. © 2017 WILEY PERIODICALS, INC.

A retrospective likelihood approach for efficient integration of multiple omics factors in case-control association studies.

PubMed

Balliu, Brunilda; Tsonaka, Roula; Boehringer, Stefan; Houwing-Duistermaat, Jeanine

2015-03-01

Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data. © 2015 Wiley Periodicals, Inc.

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting

Exact likelihood evaluations and foreground marginalization in low resolution WMAP data

NASA Astrophysics Data System (ADS)

Slosar, Anže; Seljak, Uroš; Makarov, Alexey

2004-06-01

The large scale anisotropies of Wilkinson Microwave Anisotropy Probe (WMAP) data have attracted a lot of attention and have been a source of controversy, with many favorite cosmological models being apparently disfavored by the power spectrum estimates at low l. All the existing analyses of theoretical models are based on approximations for the likelihood function, which are likely to be inaccurate on large scales. Here we present exact evaluations of the likelihood of the low multipoles by direct inversion of the theoretical covariance matrix for low resolution WMAP maps. We project out the unwanted galactic contaminants using the WMAP derived maps of these foregrounds. This improves over the template based foreground subtraction used in the original analysis, which can remove some of the cosmological signal and may lead to a suppression of power. As a result we find an increase in power at low multipoles. For the quadrupole the maximum likelihood values are rather uncertain and vary between 140 and 220 μK2. On the other hand, the probability distribution away from the peak is robust and, assuming a uniform prior between 0 and 2000 μK2, the probability of having the true value above 1200 μK2 (as predicted by the simplest cold dark matter model with a cosmological constant) is 10%, a factor of 2.5 higher than predicted by the WMAP likelihood code. We do not find the correlation function to be unusual beyond the low quadrupole value. We develop a fast likelihood evaluation routine that can be used instead of WMAP routines for low l values. We apply it to the Markov chain Monte Carlo analysis to compare the cosmological parameters between the two cases. The new analysis of WMAP either alone or jointly with the Sloan Digital Sky Survey (SDSS) and the Very Small Array (VSA) data reduces the evidence for running to less than 1σ, giving αs=-0.022±0.033 for the combined case. The new analysis prefers about a 1σ lower value of Ωm, a consequence of an increased

Assessing compatibility of direct detection data: halo-independent global likelihood analyses

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

Gelmini, Graciela B.; Huh, Ji-Haeng; Witte, Samuel J.

2016-10-18

We present two different halo-independent methods to assess the compatibility of several direct dark matter detection data sets for a given dark matter model using a global likelihood consisting of at least one extended likelihood and an arbitrary number of Gaussian or Poisson likelihoods. In the first method we find the global best fit halo function (we prove that it is a unique piecewise constant function with a number of down steps smaller than or equal to a maximum number that we compute) and construct a two-sided pointwise confidence band at any desired confidence level, which can then be comparedmore » with those derived from the extended likelihood alone to assess the joint compatibility of the data. In the second method we define a “constrained parameter goodness-of-fit” test statistic, whose p-value we then use to define a “plausibility region” (e.g. where p≥10%). For any halo function not entirely contained within the plausibility region, the level of compatibility of the data is very low (e.g. p<10%). We illustrate these methods by applying them to CDMS-II-Si and SuperCDMS data, assuming dark matter particles with elastic spin-independent isospin-conserving interactions or exothermic spin-independent isospin-violating interactions.« less

Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

PubMed

Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

2017-02-01

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.

Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis

PubMed Central

Dziak, John J.; Bray, Bethany C.; Zhang, Jieting; Zhang, Minqiang; Lanza, Stephanie T.

2016-01-01

Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004), Vermunt’s (2010) maximum likelihood (ML) approach, and the inclusive three-step approach of Bray, Lanza, & Tan (2015). These methods have been studied in the related case of latent class analysis (LCA) with categorical indicators, but not as well studied for LPA with continuous indicators. We investigated the performance of these approaches in LPA with normally distributed indicators, under different conditions of distal outcome distribution, class measurement quality, relative latent class size, and strength of association between latent class and the distal outcome. The modified BCH implemented in Latent GOLD had excellent performance. The maximum likelihood and inclusive approaches were not robust to violations of distributional assumptions. These findings broadly agree with and extend the results presented by Bakk and Vermunt (2016) in the context of LCA with categorical indicators. PMID:28630602

Marginal Maximum A Posteriori Item Parameter Estimation for the Generalized Graded Unfolding Model

ERIC Educational Resources Information Center

Roberts, James S.; Thompson, Vanessa M.

2011-01-01

A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size,…

NON-REGULAR MAXIMUM LIKELIHOOD ESTIMATION

EPA Science Inventory

Even though a body of data on the environmental occurrence of medicinal, government-approved ("ethical") pharmaceuticals has been growing over the last two decades (the subject of this book), nearly nothing is known about the disposition of illicit (illegal) drugs in th...

A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?

PubMed

Bodford, Jessica E; Kwan, Virginia S Y

2018-02-01

The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.

Empirical likelihood inference in randomized clinical trials.

PubMed

Zhang, Biao

2017-01-01

In individually randomized controlled trials, in addition to the primary outcome, information is often available on a number of covariates prior to randomization. This information is frequently utilized to undertake adjustment for baseline characteristics in order to increase precision of the estimation of average treatment effects; such adjustment is usually performed via covariate adjustment in outcome regression models. Although the use of covariate adjustment is widely seen as desirable for making treatment effect estimates more precise and the corresponding hypothesis tests more powerful, there are considerable concerns that objective inference in randomized clinical trials can potentially be compromised. In this paper, we study an empirical likelihood approach to covariate adjustment and propose two unbiased estimating functions that automatically decouple evaluation of average treatment effects from regression modeling of covariate-outcome relationships. The resulting empirical likelihood estimator of the average treatment effect is as efficient as the existing efficient adjusted estimators 1 when separate treatment-specific working regression models are correctly specified, yet are at least as efficient as the existing efficient adjusted estimators 1 for any given treatment-specific working regression models whether or not they coincide with the true treatment-specific covariate-outcome relationships. We present a simulation study to compare the finite sample performance of various methods along with some results on analysis of a data set from an HIV clinical trial. The simulation results indicate that the proposed empirical likelihood approach is more efficient and powerful than its competitors when the working covariate-outcome relationships by treatment status are misspecified.

Measurement of Absolute Concentrations of Individual Compounds in Metabolite Mixtures by Gradient-Selective Time-Zero 1H-13C HSQC (gsHSQC0) with Two Concentration References and Fast Maximum Likelihood Reconstruction Analysis

PubMed Central

Hu, Kaifeng; Ellinger, James J.; Chylla, Roger A.; Markley, John L.

2011-01-01

Time-zero 2D 13C HSQC (HSQC0) spectroscopy offers advantages over traditional 2D NMR for quantitative analysis of solutions containing a mixture of compounds because the signal intensities are directly proportional to the concentrations of the constituents. The HSQC0 spectrum is derived from a series of spectra collected with increasing repetition times within the basic HSQC block by extrapolating the repetition time to zero. Here we present an alternative approach to data collection, gradient-selective time-zero 1H-13C HSQC0 in combination with fast maximum likelihood reconstruction (FMLR) data analysis and the use of two concentration references for absolute concentration determination. Gradient-selective data acquisition results in cleaner spectra, and NMR data can be acquired in both constant-time and non-constant time mode. Semi-automatic data analysis is supported by the FMLR approach, which is used to deconvolute the spectra and extract peak volumes. The peak volumes obtained from this analysis are converted to absolute concentrations by reference to the peak volumes of two internal reference compounds of known concentration: DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at the low concentration limit (which also serves as chemical shift reference) and MES (2-(N-morpholino)ethanesulfonic acid) at the high concentration limit. The linear relationship between peak volumes and concentration is better defined with two references than with one, and the measured absolute concentrations of individual compounds in the mixture are more accurate. We compare results from semi-automated gsHSQC0 with those obtained by the original manual phase-cycled HSQC0 approach. The new approach is suitable for automatic metabolite profiling by simultaneous quantification of multiple metabolites in a complex mixture. PMID:22029275

Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey.

PubMed

Peyre, Hugo; Leplège, Alain; Coste, Joël

2011-03-01

Missing items are common in quality of life (QoL) questionnaires and present a challenge for research in this field. It remains unclear which of the various methods proposed to deal with missing data performs best in this context. We compared personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques using various realistic simulation scenarios of item missingness in QoL questionnaires constructed within the framework of classical test theory. Samples of 300 and 1,000 subjects were randomly drawn from the 2003 INSEE Decennial Health Survey (of 23,018 subjects representative of the French population and having completed the SF-36) and various patterns of missing data were generated according to three different item non-response rates (3, 6, and 9%) and three types of missing data (Little and Rubin's "missing completely at random," "missing at random," and "missing not at random"). The missing data methods were evaluated in terms of accuracy and precision for the analysis of one descriptive and one association parameter for three different scales of the SF-36. For all item non-response rates and types of missing data, multiple imputation and full information maximum likelihood appeared superior to the personal mean score and especially to hot deck in terms of accuracy and precision; however, the use of personal mean score was associated with insignificant bias (relative bias <2%) in all studied situations. Whereas multiple imputation and full information maximum likelihood are confirmed as reference methods, the personal mean score appears nonetheless appropriate for dealing with items missing from completed SF-36 questionnaires in most situations of routine use. These results can reasonably be extended to other questionnaires constructed according to classical test theory.

A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables

ERIC Educational Resources Information Center

Savalei, Victoria; Bentler, Peter M.

2009-01-01

A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a…

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

2018-07-01

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper, we use massive asymptotically optimal data compression to reduce the dimensionality of the data space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parametrized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate DELFI with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological data sets.

User's guide: Nimbus-7 Earth radiation budget narrow-field-of-view products. Scene radiance tape products, sorting into angular bins products, and maximum likelihood cloud estimation products

NASA Technical Reports Server (NTRS)

Kyle, H. Lee; Hucek, Richard R.; Groveman, Brian; Frey, Richard

1990-01-01

The archived Earth radiation budget (ERB) products produced from the Nimbus-7 ERB narrow field-of-view scanner are described. The principal products are broadband outgoing longwave radiation (4.5 to 50 microns), reflected solar radiation (0.2 to 4.8 microns), and the net radiation. Daily and monthly averages are presented on a fixed global equal area (500 sq km), grid for the period May 1979 to May 1980. Two independent algorithms are used to estimate the outgoing fluxes from the observed radiances. The algorithms are described and the results compared. The products are divided into three subsets: the Scene Radiance Tapes (SRT) contain the calibrated radiances; the Sorting into Angular Bins (SAB) tape contains the SAB produced shortwave, longwave, and net radiation products; and the Maximum Likelihood Cloud Estimation (MLCE) tapes contain the MLCE products. The tape formats are described in detail.

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers.

PubMed

Hu, Y J; Lin, D Y; Sun, W; Zeng, D

2014-10-01

Copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) co-exist throughout the human genome and jointly contribute to phenotypic variations. Thus, it is desirable to consider both types of variants, as characterized by allele-specific copy numbers (ASCNs), in association studies of complex human diseases. Current SNP genotyping technologies capture the CNV and SNP information simultaneously via fluorescent intensity measurements. The common practice of calling ASCNs from the intensity measurements and then using the ASCN calls in downstream association analysis has important limitations. First, the association tests are prone to false-positive findings when differential measurement errors between cases and controls arise from differences in DNA quality or handling. Second, the uncertainties in the ASCN calls are ignored. We present a general framework for the integrated analysis of CNVs and SNPs, including the analysis of total copy numbers as a special case. Our approach combines the ASCN calling and the association analysis into a single step while allowing for differential measurement errors. We construct likelihood functions that properly account for case-control sampling and measurement errors. We establish the asymptotic properties of the maximum likelihood estimators and develop EM algorithms to implement the corresponding inference procedures. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to a genome-wide association study of schizophrenia. Extensions to next-generation sequencing data are discussed.

Implementation and assessment of a likelihood ratio approach for the evaluation of LA-ICP-MS evidence in forensic glass analysis.

PubMed

van Es, Andrew; Wiarda, Wim; Hordijk, Maarten; Alberink, Ivo; Vergeer, Peter

2017-05-01

For the comparative analysis of glass fragments, a method using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) is in use at the NFI, giving measurements of the concentration of 18 elements. An important question is how to evaluate the results as evidence that a glass sample originates from a known glass source or from an arbitrary different glass source. One approach is the use of matching criteria e.g. based on a t-test or overlap of confidence intervals. An important drawback of this method is the fact that the rarity of the glass composition is not taken into account. A similar match can have widely different evidential values. In addition the use of fixed matching criteria can give rise to a "fall off the cliff" effect. Small differences may result in a match or a non-match. In this work a likelihood ratio system is presented, largely based on the two-level model as proposed by Aitken and Lucy [1], and Aitken, Zadora and Lucy [2]. Results show that the output from the two-level model gives good discrimination between same and different source hypotheses, but a post-hoc calibration step is necessary to improve the accuracy of the likelihood ratios. Subsequently, the robustness and performance of the LR system are studied. Results indicate that the output of the LR system is robust to the sample properties of the dataset used for calibration. Furthermore, the empirical upper and lower bound method [3], designed to deal with extrapolation errors in the density models, results in minimum and maximum values of the LR outputted by the system of 3.1×10 -3 and 3.4×10 4 . Calibration of the system, as measured by empirical cross-entropy, shows good behavior over the complete prior range. Rates of misleading evidence are small: for same-source comparisons, 0.3% of LRs support a different-source hypothesis; for different-source comparisons, 0.2% supports a same-source hypothesis. The authors use the LR system in reporting of glass cases to

A Likelihood-Based Approach to Identifying Contaminated Food Products Using Sales Data: Performance and Challenges

PubMed Central

Kaufman, James; Lessler, Justin; Harry, April; Edlund, Stefan; Hu, Kun; Douglas, Judith; Thoens, Christian; Appel, Bernd; Käsbohrer, Annemarie; Filter, Matthias

2014-01-01

Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and—in the worst cases—death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single “guilty” food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially “guilty” products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to “hard-to-identify” foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for “hard-to-identify” products. PMID:24992565

A likelihood-based approach to identifying contaminated food products using sales data: performance and challenges.

PubMed

Kaufman, James; Lessler, Justin; Harry, April; Edlund, Stefan; Hu, Kun; Douglas, Judith; Thoens, Christian; Appel, Bernd; Käsbohrer, Annemarie; Filter, Matthias

2014-07-01

Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and--in the worst cases--death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single "guilty" food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially "guilty" products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to "hard-to-identify" foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for "hard-to-identify" products.

A Computer Program for Solving a Set of Conditional Maximum Likelihood Equations Arising in the Rasch Model for Questionnaires.

ERIC Educational Resources Information Center

Andersen, Erling B.

A computer program for solving the conditional likelihood equations arising in the Rasch model for questionnaires is described. The estimation method and the computational problems involved are described in a previous research report by Andersen, but a summary of those results are given in two sections of this paper. A working example is also…

Maximum likelihood decoding analysis of Accumulate-Repeat-Accumulate Codes

NASA Technical Reports Server (NTRS)

Abbasfar, Aliazam; Divsalar, Dariush; Yao, Kung

2004-01-01

Repeat-Accumulate (RA) codes are the simplest turbo-like codes that achieve good performance. However, they cannot compete with Turbo codes or low-density parity check codes (LDPC) as far as performance is concerned. The Accumulate Repeat Accumulate (ARA) codes, as a subclass of LDPC codes, are obtained by adding a pre-coder in front of RA codes with puncturing where an accumulator is chosen as a precoder. These codes not only are very simple, but also achieve excellent performance with iterative decoding. In this paper, the performance of these codes with (ML) decoding are analyzed and compared to random codes by very tight bounds. The weight distribution of some simple ARA codes is obtained, and through existing tightest bounds we have shown the ML SNR threshold of ARA codes approaches very closely to the performance of random codes. We have shown that the use of precoder improves the SNR threshold but interleaving gain remains unchanged with respect to RA code with puncturing.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

PubMed

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

PubMed Central

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters. PMID:26820646

The Maximum Cross-Correlation approach to detecting translational motions from sequential remote-sensing images

NASA Astrophysics Data System (ADS)

Gao, J.; Lythe, M. B.

1996-06-01

This paper presents the principle of the Maximum Cross-Correlation (MCC) approach in detecting translational motions within dynamic fields from time-sequential remotely sensed images. A C program implementing the approach is presented and illustrated in a flowchart. The program is tested with a pair of sea-surface temperature images derived from Advanced Very High Resolution Radiometer (AVHRR) images near East Cape, New Zealand. Results show that the mean currents in the region have been detected satisfactorily with the approach.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Farsani, Zahra Amini; Schmid, Volker J

2017-01-01

In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role. This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available. In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study. The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently. The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI. Schattauer GmbH.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data

PubMed Central

Franco-Pedroso, Javier; Ramos, Daniel; Gonzalez-Rodriguez, Joaquin

2016-01-01

In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (Cllr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints. PMID:26901680

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.

Bayesian experimental design for models with intractable likelihoods.

PubMed

Drovandi, Christopher C; Pettitt, Anthony N

2013-12-01

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables. © 2013, The International Biometric Society.

Performance of maximum likelihood mixture models to estimate nursery habitat contributions to fish stocks: a case study on sea bream Sparus aurata

PubMed Central

Darnaude, Audrey M.

2016-01-01

Background Mixture models (MM) can be used to describe mixed stocks considering three sets of parameters: the total number of contributing sources, their chemical baseline signatures and their mixing proportions. When all nursery sources have been previously identified and sampled for juvenile fish to produce baseline nursery-signatures, mixing proportions are the only unknown set of parameters to be estimated from the mixed-stock data. Otherwise, the number of sources, as well as some/all nursery-signatures may need to be also estimated from the mixed-stock data. Our goal was to assess bias and uncertainty in these MM parameters when estimated using unconditional maximum likelihood approaches (ML-MM), under several incomplete sampling and nursery-signature separation scenarios. Methods We used a comprehensive dataset containing otolith elemental signatures of 301 juvenile Sparus aurata, sampled in three contrasting years (2008, 2010, 2011), from four distinct nursery habitats. (Mediterranean lagoons) Artificial nursery-source and mixed-stock datasets were produced considering: five different sampling scenarios where 0–4 lagoons were excluded from the nursery-source dataset and six nursery-signature separation scenarios that simulated data separated 0.5, 1.5, 2.5, 3.5, 4.5 and 5.5 standard deviations among nursery-signature centroids. Bias (BI) and uncertainty (SE) were computed to assess reliability for each of the three sets of MM parameters. Results Both bias and uncertainty in mixing proportion estimates were low (BI ≤ 0.14, SE ≤ 0.06) when all nursery-sources were sampled but exhibited large variability among cohorts and increased with the number of non-sampled sources up to BI = 0.24 and SE = 0.11. Bias and variability in baseline signature estimates also increased with the number of non-sampled sources, but tended to be less biased, and more uncertain than mixing proportion ones, across all sampling scenarios (BI < 0.13, SE < 0.29). Increasing

Maximum parsimony, substitution model, and probability phylogenetic trees.

PubMed

Weng, J F; Thomas, D A; Mareels, I

2011-01-01

The problem of inferring phylogenies (phylogenetic trees) is one of the main problems in computational biology. There are three main methods for inferring phylogenies-Maximum Parsimony (MP), Distance Matrix (DM) and Maximum Likelihood (ML), of which the MP method is the most well-studied and popular method. In the MP method the optimization criterion is the number of substitutions of the nucleotides computed by the differences in the investigated nucleotide sequences. However, the MP method is often criticized as it only counts the substitutions observable at the current time and all the unobservable substitutions that really occur in the evolutionary history are omitted. In order to take into account the unobservable substitutions, some substitution models have been established and they are now widely used in the DM and ML methods but these substitution models cannot be used within the classical MP method. Recently the authors proposed a probability representation model for phylogenetic trees and the reconstructed trees in this model are called probability phylogenetic trees. One of the advantages of the probability representation model is that it can include a substitution model to infer phylogenetic trees based on the MP principle. In this paper we explain how to use a substitution model in the reconstruction of probability phylogenetic trees and show the advantage of this approach with examples.

An Alternative Estimator for the Maximum Likelihood Estimator for the Two Extreme Response Patterns.

DTIC Science & Technology

1981-06-29

A.D-AI14# D" TENNEssEE UNIV KNOXVILLI DEPT OF PSYCHOLOGY F/6 12/1 AN ALTERNATIVE STIMATOR-FOR THE MAXIMUM LIKELIHOO ESTIMATOR F--ETCCU) JUN &I F...EXTREME RESPONSE PATTERNS 𔃺 FUMIKO SAMEJIMAr DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF TENNESSEE KNOXVILLE, TENN. 37916 JUNE, 1981 Prepared under the...contract number N00014-77-C-360, NRl 1蓺 with the Personnel and Training Research Programs Psychological Sciences Division Office of Naval Research

Maximum a posteriori decoder for digital communications

NASA Technical Reports Server (NTRS)

Altes, Richard A. (Inventor)

1997-01-01

A system and method for decoding by identification of the most likely phase coded signal corresponding to received data. The present invention has particular application to communication with signals that experience spurious random phase perturbations. The generalized estimator-correlator uses a maximum a posteriori (MAP) estimator to generate phase estimates for correlation with incoming data samples and for correlation with mean phases indicative of unique hypothesized signals. The result is a MAP likelihood statistic for each hypothesized transmission, wherein the highest value statistic identifies the transmitted signal.

76 FR 78256 - Request for Nominations of Experts for the Review of Approaches To Derive a Maximum Contaminant...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-16

... Approaches To Derive a Maximum Contaminant Level Goal for Perchlorate AGENCY: Environmental Protection Agency... maximum contaminant level goal (MCLG) for perchlorate. DATES: Nominations should be submitted by January... perchlorate. In 2011, EPA announced its decision (76 FR 7762-7767) to regulate perchlorate under the Safe...

Important factors in the maximum likelihood analysis of flight test maneuvers

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.; Montgomery, T. D.

1979-01-01

The information presented is based on the experience in the past 12 years at the NASA Dryden Flight Research Center of estimating stability and control derivatives from over 3500 maneuvers from 32 aircraft. The overall approach to the analysis of dynamic flight test data is outlined. General requirements for data and instrumentation are discussed and several examples of the types of problems that may be encountered are presented.

Maximum Likelihood Factor Analysis of the Effects of Chronic Centrifugation on the Structural Development of the Musculoskeletal System of the Rat

NASA Technical Reports Server (NTRS)

Amtmann, E.; Kimura, T.; Oyama, J.; Doden, E.; Potulski, M.

1979-01-01

At the age of 30 days female Sprague-Dawley rats were placed on a 3.66 m radius centrifuge and subsequently exposed almost continuously for 810 days to either 2.76 or 4.15 G. An age-matched control group of rats was raised near the centrifuge facility at earth gravity. Three further control groups of rats were obtained from the animal colony and sacrificed at the age of 34, 72 and 102 days. A total of 16 variables were simultaneously factor analyzed by maximum-likelihood extraction routine and the factor loadings presented after-rotation to simple structure by a varimax rotation routine. The variables include the G-load, age, body mass, femoral length and cross-sectional area, inner and outer radii, density and strength at the mid-length of the femur, dry weight of gluteus medius, semimenbranosus and triceps surae muscles. Factor analyses on A) all controls, B) all controls and the 2.76 G group, and C) all controls and centrifuged animals, produced highly similar loading structures of three common factors which accounted for 74%, 68% and 68%. respectively, of the total variance. The 3 factors were interpreted as: 1. An age and size factor which stimulates the growth in length and diameter and increases the density and strength of the femur. This factor is positively correlated with G-load but is also active in the control animals living at earth gravity. 2. A growth inhibition factor which acts on body size, femoral length and on both the outer and inner radius at mid-length of the femur. This factor is intensified by centrifugation.

A likelihood ratio test for evolutionary rate shifts and functional divergence among proteins

PubMed Central

Knudsen, Bjarne; Miyamoto, Michael M.

2001-01-01

Changes in protein function can lead to changes in the selection acting on specific residues. This can often be detected as evolutionary rate changes at the sites in question. A maximum-likelihood method for detecting evolutionary rate shifts at specific protein positions is presented. The method determines significance values of the rate differences to give a sound statistical foundation for the conclusions drawn from the analyses. A statistical test for detecting slowly evolving sites is also described. The methods are applied to a set of Myc proteins for the identification of both conserved sites and those with changing evolutionary rates. Those positions with conserved and changing rates are related to the structures and functions of their proteins. The results are compared with an earlier Bayesian method, thereby highlighting the advantages of the new likelihood ratio tests. PMID:11734650

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.

PubMed

Heersink, Daniel K; Caley, Peter; Paini, Dean R; Barry, Simon C

2016-05-01

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port-specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed-effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one-tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports. © 2015 Society for Risk Analysis.

Feature extraction of micro-motion frequency and the maximum wobble angle in a small range of missile warhead based on micro-Doppler effect

NASA Astrophysics Data System (ADS)

Li, M.; Jiang, Y. S.

2014-11-01

Micro-Doppler effect is induced by the micro-motion dynamics of the radar target itself or any structure on the target. In this paper, a simplified cone-shaped model for ballistic missile warhead with micro-nutation is established, followed by the theoretical formula of micro-nutation is derived. It is confirmed that the theoretical results are identical to simulation results by using short-time Fourier transform. Then we propose a new method for nutation period extraction via signature maximum energy fitting based on empirical mode decomposition and short-time Fourier transform. The maximum wobble angle is also extracted by distance approximate approach in a small range of wobble angle, which is combined with the maximum likelihood estimation. By the simulation studies, it is shown that these two feature extraction methods are both valid even with low signal-to-noise ratio.

Multi-Stage Maximum Likelihood Target Estimator

DTIC Science & Technology

2006-03-31

Compute the range at ti with respect to 19 the sensor associated with the ith 20 measurement (Ri): 23 1 R, = VRx ’+ Ryl’ (3 2 2.) Compute the range...the ith measurement 12 ii. Compute the range at tj with respect tothe 13 sensor associated with the ith measurement (R4) 14 = VRx + Ry (323) 15 iii

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

PubMed

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Quantum state estimation when qubits are lost: a no-data-left-behind approach

DOE PAGES

Williams, Brian P.; Lougovski, Pavel

2017-04-06

We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean and maximum likelihood estimates for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the Bayesian mean estimate for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.

Hybrid pairwise likelihood analysis of animal behavior experiments.

PubMed

Cattelan, Manuela; Varin, Cristiano

2013-12-01

The study of the determinants of fights between animals is an important issue in understanding animal behavior. For this purpose, tournament experiments among a set of animals are often used by zoologists. The results of these tournament experiments are naturally analyzed by paired comparison models. Proper statistical analysis of these models is complicated by the presence of dependence between the outcomes of fights because the same animal is involved in different contests. This paper discusses two different model specifications to account for between-fights dependence. Models are fitted through the hybrid pairwise likelihood method that iterates between optimal estimating equations for the regression parameters and pairwise likelihood inference for the association parameters. This approach requires the specification of means and covariances only. For this reason, the method can be applied also when the computation of the joint distribution is difficult or inconvenient. The proposed methodology is investigated by simulation studies and applied to real data about adult male Cape Dwarf Chameleons. © 2013, The International Biometric Society.

Inverse Ising problem in continuous time: A latent variable approach

NASA Astrophysics Data System (ADS)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2014-01-01

This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…

Lord's Wald Test for Detecting Dif in Multidimensional Irt Models: A Comparison of Two Estimation Approaches

ERIC Educational Resources Information Center

Lee, Soo; Suh, Youngsuk

2018-01-01

Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect…

Maximum likelihood analysis of bioassay data from long-term follow-up of two refractory PuO2 inhalation cases.

PubMed

Avtandilashvili, Maia; Brey, Richard; James, Anthony C

2012-07-01

The U.S. Transuranium and Uranium Registries' tissue donors 0202 and 0407 are the two most highly exposed of the 18 registrants who were involved in the 1965 plutonium fire accident at a defense nuclear facility. Material released during the fire was well characterized as "high fired" refractory plutonium dioxide with 0.32-μm mass median diameter. The extensive bioassay data from long-term follow-up of these two cases were used to evaluate the applicability of the Human Respiratory Tract Model presented by International Commission on Radiological Protection in Publication 66 and its revision proposed by Gregoratto et al. in order to account for the observed long-term retention of insoluble material in the lungs. The maximum likelihood method was used to calculate the point estimates of intake and tissue doses and to examine the effect of different lung clearance, blood absorption, and systemic models on the goodness-of-fit and estimated dose values. With appropriate adjustments, Gregoratto et al. particle transport model coupled with the customized blood absorption parameters yielded a credible fit to the bioassay data for both cases and predicted the Case 0202 liver and skeletal activities measured postmortem. PuO2 particles produced by the plutonium fire are extremely insoluble. About 1% of this material is absorbed from the respiratory tract relatively rapidly, at a rate of about 1 to 2 d (half-time about 8 to 16 h). The remainder (99%) is absorbed extremely slowly, at a rate of about 5 × 10(-6) d (half-time about 400 y). When considering this situation, it appears that doses to other body organs are negligible in comparison to those to tissues of the respiratory tract. About 96% of the total committed weighted dose equivalent is contributed by the lungs. Doses absorbed by these workers' lungs were high: 3.2 Gy to AI and 6.5 Gy to LNTH for Case 0202 (18 y post-intake) and 3.2 Gy to AI and 55.5 Gy to LNTH for Case 0407 (43 y post-intake). This evaluation

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

Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood

PubMed Central

Bondell, Howard D.; Stefanski, Leonard A.

2013-01-01

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

Using Maximum Likelihood Statistical Methods and Pigment Flux Data to Constrain Particle Exchange and Organic Matter Remineralization Rate Constants in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Wang, W.; Lee, C.; Cochran, K. K.; Armstrong, R. A.

2016-02-01

Sinking particles play a pivotal role transferring material from the surface to the deeper ocean via the "biological pump". To quantify the extent to which these particles aggregate and disaggregate, and thus affect particle settling velocity, we constructed a box model to describe organic matter cycling. The box model was fit to chloropigment data sampled in the 2005 MedFlux project using Indented Rotating Sphere sediment traps operating in Settling Velocity (SV) mode. Because of the very different pigment compositions of phytoplankton and fecal pellets, chloropigments are useful as proxies to record particle exchange. The maximum likelihood statistical method was used to estimate particle aggregation, disaggregation, and organic matter remineralization rate constants. Eleven settling velocity categories collected by SV sediment traps were grouped into two sinking velocity classes (fast- and slow-sinking) to decrease the number of parameters that needed to be estimated. Organic matter degradation rate constants were estimated to be 1.2, 1.6, and 1.1 y^-1, which are equivalent to degradation half-lives of 0.60, 0.45, and 0.62 y^-1, at 313, 524, and 1918 m, respectively. Rate constants of chlorophyll a degradation to pheopigments (pheophorbide, pheophytin, and pyropheophorbide) were estimated to be 0.88, 0.93, and 1.2 y^-1, at 313, 524, and 1918 m, respectively. Aggregation rate constants varied little with depth, with the highest value being 0.07 y^-1 at 524 m. Disaggregation rate constants were highest at 524 m (14 y^-1) and lowest at 1918 m (9.6 y^-1)

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

NASA Astrophysics Data System (ADS)

Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng

2016-09-01

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

NASA Astrophysics Data System (ADS)

Storm, Emma; Weniger, Christoph; Calore, Francesca

2017-08-01

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (gtrsim 105) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that are motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |l|<90o and |b|<20o, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.

Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States

USGS Publications Warehouse

Eng, Kenny; Carlisle, Daren M.; Wolock, David M.; Falcone, James A.

2013-01-01

An approach is presented in this study to aid water-resource managers in characterizing streamflow alteration at ungauged rivers. Such approaches can be used to take advantage of the substantial amounts of biological data collected at ungauged rivers to evaluate the potential ecological consequences of altered streamflows. National-scale random forest statistical models are developed to predict the likelihood that ungauged rivers have altered streamflows (relative to expected natural condition) for five hydrologic metrics (HMs) representing different aspects of the streamflow regime. The models use human disturbance variables, such as number of dams and road density, to predict the likelihood of streamflow alteration. For each HM, separate models are derived to predict the likelihood that the observed metric is greater than (‘inflated’) or less than (‘diminished’) natural conditions. The utility of these models is demonstrated by applying them to all river segments in the South Platte River in Colorado, USA, and for all 10-digit hydrologic units in the conterminous United States. In general, the models successfully predicted the likelihood of alteration to the five HMs at the national scale as well as in the South Platte River basin. However, the models predicting the likelihood of diminished HMs consistently outperformed models predicting inflated HMs, possibly because of fewer sites across the conterminous United States where HMs are inflated. The results of these analyses suggest that the primary predictors of altered streamflow regimes across the Nation are (i) the residence time of annual runoff held in storage in reservoirs, (ii) the degree of urbanization measured by road density and (iii) the extent of agricultural land cover in the river basin.

Predicting Tree Mortality From Diameter Growth: A Comparison of Maximum Likelihood and Bayesian Approaches

Treesearch

Peter H. Wychoff; James S. Clark

2000-01-01

Ecologists and foresters have long noted a link between tree growth rate and mortality, and recent work suggests that i&erspecific differences in low growth tolerauce is a key force shaping forest structure. Little information is available, however, on the growth-mortality relationship for most species. We present three methods for estimating growth-mortality...

Exploration of the Maximum Entropy/Optimal Projection Approach to Control Design Synthesis for Large Space Structures.

DTIC Science & Technology

1985-02-01

Energy Analysis , a branch of dynamic modal analysis developed for analyzing acoustic vibration problems, its present stage of development embodies a...Maximum Entropy Stochastic Modelling and Reduced-Order Design Synthesis is a rigorous new approach to this class of problems. Inspired by Statistical

A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

ERIC Educational Resources Information Center

Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini

2012-01-01

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…

Exponential series approaches for nonparametric graphical models

NASA Astrophysics Data System (ADS)

Janofsky, Eric

Markov Random Fields (MRFs) or undirected graphical models are parsimonious representations of joint probability distributions. This thesis studies high-dimensional, continuous-valued pairwise Markov Random Fields. We are particularly interested in approximating pairwise densities whose logarithm belongs to a Sobolev space. For this problem we propose the method of exponential series which approximates the log density by a finite-dimensional exponential family with the number of sufficient statistics increasing with the sample size. We consider two approaches to estimating these models. The first is regularized maximum likelihood. This involves optimizing the sum of the log-likelihood of the data and a sparsity-inducing regularizer. We then propose a variational approximation to the likelihood based on tree-reweighted, nonparametric message passing. This approximation allows for upper bounds on risk estimates, leverages parallelization and is scalable to densities on hundreds of nodes. We show how the regularized variational MLE may be estimated using a proximal gradient algorithm. We then consider estimation using regularized score matching. This approach uses an alternative scoring rule to the log-likelihood, which obviates the need to compute the normalizing constant of the distribution. For general continuous-valued exponential families, we provide parameter and edge consistency results. As a special case we detail a new approach to sparse precision matrix estimation which has statistical performance competitive with the graphical lasso and computational performance competitive with the state-of-the-art glasso algorithm. We then describe results for model selection in the nonparametric pairwise model using exponential series. The regularized score matching problem is shown to be a convex program; we provide scalable algorithms based on consensus alternating direction method of multipliers (ADMM) and coordinate-wise descent. We use simulations to compare our

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

NASA Astrophysics Data System (ADS)

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

2013-02-01

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

Likelihoods for fixed rank nomination networks

PubMed Central

HOFF, PETER; FOSDICK, BAILEY; VOLFOVSKY, ALEX; STOVEL, KATHERINE

2014-01-01

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design. PMID:25110586

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction

PubMed Central

Williamson, Ross S.; Sahani, Maneesh; Pillow, Jonathan W.

2015-01-01

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron’s probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as “single-spike information” to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. PMID:25831448

Maximum performance synergy: A new approach to recording studio control room design

NASA Astrophysics Data System (ADS)

Szymanski, Jeff D.

2003-10-01

Popular recording studio control room designs include LEDE(tm), RFZ(tm), and nonenvironment rooms. The common goal of all of these is to create an accurate acoustical environment that does not distort or otherwise color audio reproduction. Also common to these designs is the frequent need to have multiple ancillary recording rooms, often adjacent to the main control room, where group members perform. This approach, where group members are physically separated from one another, can lead to lack of ensemble in the finished recordings. New twists on old acoustical treatment techniques have been implemented at a studio in Nashville, Tennessee, which minimize the need for multiple ancillary recording rooms, thus creating an environment where talent, producer and recording professionals can all occupy the same space for maximum performance synergy. Semi-separated performance areas are designed around a central, critical listening area. The techniques and equipment required to achieve this separation are reviewed, as are advantages and disadvantages to this new control room design approach.

Photon Counting Data Analysis: Application of the Maximum Likelihood and Related Methods for the Determination of Lifetimes in Mixtures of Rose Bengal and Rhodamine B

DOE PAGES

Santra, Kalyan; Smith, Emily A.; Petrich, Jacob W.; ...

2016-12-12

It is often convenient to know the minimum amount of data needed in order to obtain a result of desired accuracy and precision. It is a necessity in the case of subdiffraction-limited microscopies, such as stimulated emission depletion (STED) microscopy, owing to the limited sample volumes and the extreme sensitivity of the samples to photobleaching and photodamage. We present a detailed comparison of probability-based techniques (the maximum likelihood method and methods based on the binomial and the Poisson distributions) with residual minimization-based techniques for retrieving the fluorescence decay parameters for various two-fluorophore mixtures, as a function of the total numbermore » of photon counts, in time-correlated, single-photon counting experiments. The probability-based techniques proved to be the most robust (insensitive to initial values) in retrieving the target parameters and, in fact, performed equivalently to 2-3 significant figures. This is to be expected, as we demonstrate that the three methods are fundamentally related. Furthermore, methods based on the Poisson and binomial distributions have the desirable feature of providing a bin-by-bin analysis of a single fluorescence decay trace, which thus permits statistics to be acquired using only the one trace for not only the mean and median values of the fluorescence decay parameters but also for the associated standard deviations. Lastly, these probability-based methods lend themselves well to the analysis of the sparse data sets that are encountered in subdiffraction-limited microscopies.« less

Section 9: Ground Water - Likelihood of Release

EPA Pesticide Factsheets

HRS training. the ground water pathway likelihood of release factor category reflects the likelihood that there has been, or will be, a release of hazardous substances in any of the aquifers underlying the site.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

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

Storm, Emma; Weniger, Christoph; Calore, Francesca, E-mail: e.m.storm@uva.nl, E-mail: c.weniger@uva.nl, E-mail: francesca.calore@lapth.cnrs.fr

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (∼> 10{sup 5}) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that aremore » motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |ℓ|<90{sup o} and | b |<20{sup o}, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.« less

Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies

NASA Astrophysics Data System (ADS)

Klein, Iris M.; Rousseau, Alain N.; Frigon, Anne; Freudiger, Daphné; Gagnon, Patrick

2016-06-01

Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation. This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability.

Maximum angular accuracy of pulsed laser radar in photocounting limit.

PubMed

Elbaum, M; Diament, P; King, M; Edelson, W

1977-07-01

To estimate the angular position of targets with pulsed laser radars, their images may be sensed with a fourquadrant noncoherent detector and the image photocounting distribution processed to obtain the angular estimates. The limits imposed on the accuracy of angular estimation by signal and background radiation shot noise, dark current noise, and target cross-section fluctuations are calculated. Maximum likelihood estimates of angular positions are derived for optically rough and specular targets and their performances compared with theoretical lower bounds.

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

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

Automatic optimism: the affective basis of judgments about the likelihood of future events.

PubMed

Lench, Heather C

2009-05-01

People generally judge that the future will be consistent with their desires, but the reason for this desirability bias is unclear. This investigation examined whether affective reactions associated with future events are the mechanism through which desires influence likelihood judgments. In 4 studies, affective reactions were manipulated for initially neutral events. Compared with a neutral condition, events associated with positive reactions were judged as likely to occur, and events associated with negative reactions were judged as unlikely to occur. Desirability biases were reduced when participants could misattribute affective reactions to a source other than future events, and the relationship between affective reactions and judgments was influenced when approach and avoidance motivations were independently manipulated. Together, these findings demonstrate that positive and negative affective reactions to potential events cause the desirability bias in likelihood judgments and suggest that this effect occurs because of a tendency to approach positive possibilities and avoid negative possibilities. (c) 2009 APA, all rights reserved.

Nonlinear Statistical Estimation with Numerical Maximum Likelihood

DTIC Science & Technology

1974-10-01

probably most directly attributable to the speed, precision and compactness of the linear programming algorithm exercised ; the mutual primal-dual...discriminant analysis is to classify the individual as a member of T# or IT, 1 2 according to the relative...Introduction to the Dissertation 1 Introduction to Statistical Estimation Theory 3 Choice of Estimator.. .Density Functions 12 Choice of Estimator

Speech processing using maximum likelihood continuity mapping

DOEpatents

Hogden, John E.

2000-01-01

Speech processing is obtained that, given a probabilistic mapping between static speech sounds and pseudo-articulator positions, allows sequences of speech sounds to be mapped to smooth sequences of pseudo-articulator positions. In addition, a method for learning a probabilistic mapping between static speech sounds and pseudo-articulator position is described. The method for learning the mapping between static speech sounds and pseudo-articulator position uses a set of training data composed only of speech sounds. The said speech processing can be applied to various speech analysis tasks, including speech recognition, speaker recognition, speech coding, speech synthesis, and voice mimicry.

Speech processing using maximum likelihood continuity mapping

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

Hogden, J.E.

Speech processing is obtained that, given a probabilistic mapping between static speech sounds and pseudo-articulator positions, allows sequences of speech sounds to be mapped to smooth sequences of pseudo-articulator positions. In addition, a method for learning a probabilistic mapping between static speech sounds and pseudo-articulator position is described. The method for learning the mapping between static speech sounds and pseudo-articulator position uses a set of training data composed only of speech sounds. The said speech processing can be applied to various speech analysis tasks, including speech recognition, speaker recognition, speech coding, speech synthesis, and voice mimicry.

Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

PubMed

Posada, David; Buckley, Thomas R

2004-10-01

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).

Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

USGS Publications Warehouse

Lentz, Erika E.; Thieler, E. Robert; Plant, Nathaniel G.; Stippa, Sawyer R.; Horton, Radley M.; Gesch, Dean B.

2016-01-01

Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Evaluation of Dynamic Coastal Response to Sea-level Rise Modifies Inundation Likelihood

NASA Technical Reports Server (NTRS)

Lentz, Erika E.; Thieler, E. Robert; Plant, Nathaniel G.; Stippa, Sawyer R.; Horton, Radley M.; Gesch, Dean B.

2016-01-01

Sea-level rise (SLR) poses a range of threats to natural and built environments, making assessments of SLR-induced hazards essential for informed decision making. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30m resolution predictions for more than 38,000 sq km of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Probabilistic evaluation of n traces with no putative source: A likelihood ratio based approach in an investigative framework.

PubMed

De March, I; Sironi, E; Taroni, F

2016-09-01

Analysis of marks recovered from different crime scenes can be useful to detect a linkage between criminal cases, even though a putative source for the recovered traces is not available. This particular circumstance is often encountered in the early stage of investigations and thus, the evaluation of evidence association may provide useful information for the investigators. This association is evaluated here from a probabilistic point of view: a likelihood ratio based approach is suggested in order to quantify the strength of the evidence of trace association in the light of two mutually exclusive propositions, namely that the n traces come from a common source or from an unspecified number of sources. To deal with this kind of problem, probabilistic graphical models are used, in form of Bayesian networks and object-oriented Bayesian networks, allowing users to intuitively handle with uncertainty related to the inferential problem. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.

PubMed

Liu, Wei; Zhang, Bo; Zhang, Hui; Zhang, Zhiwei

2017-04-01

There is growing interest in assessing immune biomarkers, which are quick to measure and potentially predictive of long-term efficacy, as surrogate endpoints in randomized, placebo-controlled vaccine trials. This can be done under a principal stratification approach, with principal strata defined using a subject's potential immune responses to vaccine and placebo (the latter may be assumed to be zero). In this context, principal surrogacy refers to the extent to which vaccine efficacy varies across principal strata. Because a placebo recipient's potential immune response to vaccine is unobserved in a standard vaccine trial, augmented vaccine trials have been proposed to produce the information needed to evaluate principal surrogacy. This article reviews existing methods based on an estimated likelihood and a pseudo-score (PS) and proposes two new methods based on a semiparametric likelihood (SL) and a pseudo-likelihood (PL), for analyzing augmented vaccine trials. Unlike the PS method, the SL method does not require a model for missingness, which can be advantageous when immune response data are missing by happenstance. The SL method is shown to be asymptotically efficient, and it performs similarly to the PS and PL methods in simulation experiments. The PL method appears to have a computational advantage over the PS and SL methods.

Differentiating the Differentiation Models: A Comparison of the Retrieving Effectively from Memory Model (REM) and the Subjective Likelihood Model (SLiM)

ERIC Educational Resources Information Center

Criss, Amy H.; McClelland, James L.

2006-01-01

The subjective likelihood model [SLiM; McClelland, J. L., & Chappell, M. (1998). Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory. "Psychological Review," 105(4), 734-760.] and the retrieving effectively from memory model [REM; Shiffrin, R. M., & Steyvers, M. (1997). A model…

An efficient algorithm for accurate computation of the Dirichlet-multinomial log-likelihood function.

PubMed

Yu, Peng; Shaw, Chad A

2014-06-01

The Dirichlet-multinomial (DMN) distribution is a fundamental model for multicategory count data with overdispersion. This distribution has many uses in bioinformatics including applications to metagenomics data, transctriptomics and alternative splicing. The DMN distribution reduces to the multinomial distribution when the overdispersion parameter ψ is 0. Unfortunately, numerical computation of the DMN log-likelihood function by conventional methods results in instability in the neighborhood of [Formula: see text]. An alternative formulation circumvents this instability, but it leads to long runtimes that make it impractical for large count data common in bioinformatics. We have developed a new method for computation of the DMN log-likelihood to solve the instability problem without incurring long runtimes. The new approach is composed of a novel formula and an algorithm to extend its applicability. Our numerical experiments show that this new method both improves the accuracy of log-likelihood evaluation and the runtime by several orders of magnitude, especially in high-count data situations that are common in deep sequencing data. Using real metagenomic data, our method achieves manyfold runtime improvement. Our method increases the feasibility of using the DMN distribution to model many high-throughput problems in bioinformatics. We have included in our work an R package giving access to this method and a vingette applying this approach to metagenomic data. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

NASA Astrophysics Data System (ADS)

Li, Li; Yao, Jian; Xie, Renping; Tu, Jinge; Feng, Chen

2016-06-01

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

Robust Likelihoods for Inflationary Gravitational Waves from Maps of Cosmic Microwave Background Polarization

NASA Technical Reports Server (NTRS)

Switzer, Eric Ryan; Watts, Duncan J.

2016-01-01

The B-mode polarization of the cosmic microwave background provides a unique window into tensor perturbations from inflationary gravitational waves. Survey effects complicate the estimation and description of the power spectrum on the largest angular scales. The pixel-space likelihood yields parameter distributions without the power spectrum as an intermediate step, but it does not have the large suite of tests available to power spectral methods. Searches for primordial B-modes must rigorously reject and rule out contamination. Many forms of contamination vary or are uncorrelated across epochs, frequencies, surveys, or other data treatment subsets. The cross power and the power spectrum of the difference of subset maps provide approaches to reject and isolate excess variance. We develop an analogous joint pixel-space likelihood. Contamination not modeled in the likelihood produces parameter-dependent bias and complicates the interpretation of the difference map. We describe a null test that consistently weights the difference map. Excess variance should either be explicitly modeled in the covariance or be removed through reprocessing the data.

The Dud-Alternative Effect in Likelihood Judgment

ERIC Educational Resources Information Center

Windschitl, Paul D.; Chambers, John R.

2004-01-01

The judged likelihood of a focal outcome should generally decrease as the list of alternative possibilities increases. For example, the likelihood that a runner will win a race goes down when 2 new entries are added to the field. However, 6 experiments demonstrate that the presence of implausible alternatives (duds) often increases the judged…

Likelihood Ratio Tests for Special Rasch Models

ERIC Educational Resources Information Center

Hessen, David J.

2010-01-01

In this article, a general class of special Rasch models for dichotomous item scores is considered. Although Andersen's likelihood ratio test can be used to test whether a Rasch model fits to the data, the test does not differentiate between special Rasch models. Therefore, in this article, new likelihood ratio tests are proposed for testing…

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

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1984-01-01

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

What controls the maximum magnitude of injection-induced earthquakes?

NASA Astrophysics Data System (ADS)

Eaton, D. W. S.

2017-12-01

Three different approaches for estimation of maximum magnitude are considered here, along with their implications for managing risk. The first approach is based on a deterministic limit for seismic moment proposed by McGarr (1976), which was originally designed for application to mining-induced seismicity. This approach has since been reformulated for earthquakes induced by fluid injection (McGarr, 2014). In essence, this method assumes that the upper limit for seismic moment release is constrained by the pressure-induced stress change. A deterministic limit is given by the product of shear modulus and the net injected fluid volume. This method is based on the assumptions that the medium is fully saturated and in a state of incipient failure. An alternative geometrical approach was proposed by Shapiro et al. (2011), who postulated that the rupture area for an induced earthquake falls entirely within the stimulated volume. This assumption reduces the maximum-magnitude problem to one of estimating the largest potential slip surface area within a given stimulated volume. Finally, van der Elst et al. (2016) proposed that the maximum observed magnitude, statistically speaking, is the expected maximum value for a finite sample drawn from an unbounded Gutenberg-Richter distribution. These three models imply different approaches for risk management. The deterministic method proposed by McGarr (2014) implies that a ceiling on the maximum magnitude can be imposed by limiting the net injected volume, whereas the approach developed by Shapiro et al. (2011) implies that the time-dependent maximum magnitude is governed by the spatial size of the microseismic event cloud. Finally, the sample-size hypothesis of Van der Elst et al. (2016) implies that the best available estimate of the maximum magnitude is based upon observed seismicity rate. The latter two approaches suggest that real-time monitoring is essential for effective management of risk. A reliable estimate of maximum

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

PubMed

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Improvement and comparison of likelihood functions for model calibration and parameter uncertainty analysis within a Markov chain Monte Carlo scheme

NASA Astrophysics Data System (ADS)

Cheng, Qin-Bo; Chen, Xi; Xu, Chong-Yu; Reinhardt-Imjela, Christian; Schulte, Achim

2014-11-01

In this study, the likelihood functions for uncertainty analysis of hydrological models are compared and improved through the following steps: (1) the equivalent relationship between the Nash-Sutcliffe Efficiency coefficient (NSE) and the likelihood function with Gaussian independent and identically distributed residuals is proved; (2) a new estimation method of the Box-Cox transformation (BC) parameter is developed to improve the effective elimination of the heteroscedasticity of model residuals; and (3) three likelihood functions-NSE, Generalized Error Distribution with BC (BC-GED) and Skew Generalized Error Distribution with BC (BC-SGED)-are applied for SWAT-WB-VSA (Soil and Water Assessment Tool - Water Balance - Variable Source Area) model calibration in the Baocun watershed, Eastern China. Performances of calibrated models are compared using the observed river discharges and groundwater levels. The result shows that the minimum variance constraint can effectively estimate the BC parameter. The form of the likelihood function significantly impacts on the calibrated parameters and the simulated results of high and low flow components. SWAT-WB-VSA with the NSE approach simulates flood well, but baseflow badly owing to the assumption of Gaussian error distribution, where the probability of the large error is low, but the small error around zero approximates equiprobability. By contrast, SWAT-WB-VSA with the BC-GED or BC-SGED approach mimics baseflow well, which is proved in the groundwater level simulation. The assumption of skewness of the error distribution may be unnecessary, because all the results of the BC-SGED approach are nearly the same as those of the BC-GED approach.

Multiple robustness in factorized likelihood models.

PubMed

Molina, J; Rotnitzky, A; Sued, M; Robins, J M

2017-09-01

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.

Stochastic capture zone analysis of an arsenic-contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology

NASA Astrophysics Data System (ADS)

Morse, Brad S.; Pohll, Greg; Huntington, Justin; Rodriguez Castillo, Ramiro

2003-06-01

In 1992, Mexican researchers discovered concentrations of arsenic in excess of World Heath Organization (WHO) standards in several municipal wells in the Zimapan Valley of Mexico. This study describes a method to delineate a capture zone for one of the most highly contaminated wells to aid in future well siting. A stochastic approach was used to model the capture zone because of the high level of uncertainty in several input parameters. Two stochastic techniques were performed and compared: "standard" Monte Carlo analysis and the generalized likelihood uncertainty estimator (GLUE) methodology. The GLUE procedure differs from standard Monte Carlo analysis in that it incorporates a goodness of fit (termed a likelihood measure) in evaluating the model. This allows for more information (in this case, head data) to be used in the uncertainty analysis, resulting in smaller prediction uncertainty. Two likelihood measures are tested in this study to determine which are in better agreement with the observed heads. While the standard Monte Carlo approach does not aid in parameter estimation, the GLUE methodology indicates best fit models when hydraulic conductivity is approximately 10-6.5 m/s, with vertically isotropic conditions and large quantities of interbasin flow entering the basin. Probabilistic isochrones (capture zone boundaries) are then presented, and as predicted, the GLUE-derived capture zones are significantly smaller in area than those from the standard Monte Carlo approach.

NLSCIDNT user's guide maximum likehood parameter identification computer program with nonlinear rotorcraft model

NASA Technical Reports Server (NTRS)

1979-01-01

A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.

Assessing Individual Weather Risk-Taking and Its Role in Modeling Likelihood of Hurricane Evacuation

NASA Astrophysics Data System (ADS)

Stewart, A. E.

2017-12-01

This research focuses upon measuring an individual's level of perceived risk of different severe and extreme weather conditions using a new self-report measure, the Weather Risk-Taking Scale (WRTS). For 32 severe and extreme situations in which people could perform an unsafe behavior (e. g., remaining outside with lightning striking close by, driving over roadways covered with water, not evacuating ahead of an approaching hurricane, etc.), people rated: 1.their likelihood of performing the behavior, 2. The perceived risk of performing the behavior, 3. the expected benefits of performing the behavior, and 4. whether the behavior has actually been performed in the past. Initial development research with the measure using 246 undergraduate students examined its psychometric properties and found that it was internally consistent (Cronbach's a ranged from .87 to .93 for the four scales) and that the scales possessed good temporal (test-retest) reliability (r's ranged from .84 to .91). A second regression study involving 86 undergraduate students found that taking weather risks was associated with having taken similar risks in one's past and with the personality trait of sensation-seeking. Being more attentive to the weather and perceiving its risks when it became extreme was associated with lower likelihoods of taking weather risks (overall regression model, R2adj = 0.60). A third study involving 334 people examined the contributions of weather risk perceptions and risk-taking in modeling the self-reported likelihood of complying with a recommended evacuation ahead of a hurricane. Here, higher perceptions of hurricane risks and lower perceived benefits of risk-taking along with fear of severe weather and hurricane personal self-efficacy ratings were all statistically significant contributors to the likelihood of evacuating ahead of a hurricane. Psychological rootedness and attachment to one's home also tend to predict lack of evacuation. This research highlights the

Workplace air measurements and likelihood of exposure to manufactured nano-objects, agglomerates, and aggregates

NASA Astrophysics Data System (ADS)

Brouwer, Derk H.; van Duuren-Stuurman, Birgit; Berges, Markus; Bard, Delphine; Jankowska, Elzbieta; Moehlmann, Carsten; Pelzer, Johannes; Mark, Dave

2013-11-01

Manufactured nano-objects, agglomerates, and aggregates (NOAA) may have adverse effect on human health, but little is known about occupational risks since actual estimates of exposure are lacking. In a large-scale workplace air-monitoring campaign, 19 enterprises were visited and 120 potential exposure scenarios were measured. A multi-metric exposure assessment approach was followed and a decision logic was developed to afford analysis of all results in concert. The overall evaluation was classified by categories of likelihood of exposure. At task level about 53 % showed increased particle number or surface area concentration compared to "background" level, whereas 72 % of the TEM samples revealed an indication that NOAA were present in the workplace. For 54 out of the 120 task-based exposure scenarios, an overall evaluation could be made based on all parameters of the decision logic. For only 1 exposure scenario (approximately 2 %), the highest level of potential likelihood was assigned, whereas in total in 56 % of the exposure scenarios the overall evaluation revealed the lowest level of likelihood. However, for the remaining 42 % exposure to NOAA could not be excluded.

Mortality table construction

NASA Astrophysics Data System (ADS)

Sutawanir

2015-12-01

Mortality tables play important role in actuarial studies such as life annuities, premium determination, premium reserve, valuation pension plan, pension funding. Some known mortality tables are CSO mortality table, Indonesian Mortality Table, Bowers mortality table, Japan Mortality table. For actuary applications some tables are constructed with different environment such as single decrement, double decrement, and multiple decrement. There exist two approaches in mortality table construction : mathematics approach and statistical approach. Distribution model and estimation theory are the statistical concepts that are used in mortality table construction. This article aims to discuss the statistical approach in mortality table construction. The distributional assumptions are uniform death distribution (UDD) and constant force (exponential). Moment estimation and maximum likelihood are used to estimate the mortality parameter. Moment estimation methods are easier to manipulate compared to maximum likelihood estimation (mle). However, the complete mortality data are not used in moment estimation method. Maximum likelihood exploited all available information in mortality estimation. Some mle equations are complicated and solved using numerical methods. The article focus on single decrement estimation using moment and maximum likelihood estimation. Some extension to double decrement will introduced. Simple dataset will be used to illustrated the mortality estimation, and mortality table.

Expressed Likelihood as Motivator: Creating Value through Engaging What’s Real

PubMed Central

Higgins, E. Tory; Franks, Becca; Pavarini, Dana; Sehnert, Steen; Manley, Katie

2012-01-01

Our research tested two predictions regarding how likelihood can have motivational effects as a function of how a probability is expressed. We predicted that describing the probability of a future event that could be either A or B using the language of high likelihood (“80% A”) rather than low likelihood (“20% B”), i.e., high rather than low expressed likelihood, would make a present activity more real and engaging, as long as the future event had properties relevant to the present activity. We also predicted that strengthening engagement from the high (vs. low) expressed likelihood of a future event would intensify the value of present positive and negative objects (in opposite directions). Both predictions were supported. There was also evidence that this intensification effect from expressed likelihood was independent of the actual probability or valence of the future event. What mattered was whether high versus low likelihood language was used to describe the future event. PMID:23940411

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

NASA Astrophysics Data System (ADS)

Mat Jan, Nur Amalina; Shabri, Ani

2017-01-01