WorldWideScience

Sample records for maximum likelihood-based methods

  1. Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies.

    Science.gov (United States)

    Rukhin, Andrew L

    2011-01-01

    A formulation of the problem of combining data from several sources is discussed in terms of random effects models. The unknown measurement precision is assumed not to be the same for all methods. We investigate maximum likelihood solutions in this model. By representing the likelihood equations as simultaneous polynomial equations, the exact form of the Groebner basis for their stationary points is derived when there are two methods. A parametrization of these solutions which allows their comparison is suggested. A numerical method for solving likelihood equations is outlined, and an alternative to the maximum likelihood method, the restricted maximum likelihood, is studied. In the situation when methods variances are considered to be known an upper bound on the between-method variance is obtained. The relationship between likelihood equations and moment-type equations is also discussed.

  2. Maximum-likelihood methods for array processing based on time-frequency distributions

    Science.gov (United States)

    Zhang, Yimin; Mu, Weifeng; Amin, Moeness G.

    1999-11-01

    This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non- stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi- dimensional optimizations. Compared to the recently proposed time- frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.

  3. Maximum Likelihood-Based Methods for Target Velocity Estimation with Distributed MIMO Radar

    Directory of Open Access Journals (Sweden)

    Zhenxin Cao

    2018-02-01

    Full Text Available The estimation problem for target velocity is addressed in this in the scenario with a distributed multi-input multi-out (MIMO radar system. A maximum likelihood (ML-based estimation method is derived with the knowledge of target position. Then, in the scenario without the knowledge of target position, an iterative method is proposed to estimate the target velocity by updating the position information iteratively. Moreover, the Carmér-Rao Lower Bounds (CRLBs for both scenarios are derived, and the performance degradation of velocity estimation without the position information is also expressed. Simulation results show that the proposed estimation methods can approach the CRLBs, and the velocity estimation performance can be further improved by increasing either the number of radar antennas or the information accuracy of the target position. Furthermore, compared with the existing methods, a better estimation performance can be achieved.

  4. Experimental demonstration of the maximum likelihood-based chromatic dispersion estimator for coherent receivers

    DEFF Research Database (Denmark)

    Borkowski, Robert; Johannisson, Pontus; Wymeersch, Henk

    2014-01-01

    We perform an experimental investigation of a maximum likelihood-based (ML-based) algorithm for bulk chromatic dispersion estimation for digital coherent receivers operating in uncompensated optical networks. We demonstrate the robustness of the method at low optical signal-to-noise ratio (OSNR...

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

    Science.gov (United States)

    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

  6. Maximum-likelihood method for numerical inversion of Mellin transform

    International Nuclear Information System (INIS)

    Iqbal, M.

    1997-01-01

    A method is described for inverting the Mellin transform which uses an expansion in Laguerre polynomials and converts the Mellin transform to Laplace transform, then the maximum-likelihood regularization method is used to recover the original function of the Mellin transform. The performance of the method is illustrated by the inversion of the test functions available in the literature (J. Inst. Math. Appl., 20 (1977) 73; Math. Comput., 53 (1989) 589). Effectiveness of the method is shown by results obtained through demonstration by means of tables and diagrams

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Waligorska, Marta [Adam Mickiewicz University, Faculty of Chemistry, Grunwaldzka 6, 60-780 Poznan (Poland); Molski, Andrzej, E-mail: amolski@amu.edu.pl [Adam Mickiewicz University, Faculty of Chemistry, Grunwaldzka 6, 60-780 Poznan (Poland)

    2012-07-25

    Highlights: Black-Right-Pointing-Pointer We study model selection and parameter recovery from single-molecule FRET experiments. Black-Right-Pointing-Pointer We examine the maximum likelihood-based analysis of two-color photon trajectories. Black-Right-Pointing-Pointer The number of observed photons determines the performance of the method. Black-Right-Pointing-Pointer For long trajectories, one can extract mean dwell times that are comparable to inter-photon times. -- Abstract: When two fluorophores (donor and acceptor) are attached to an immobilized biomolecule, anti-correlated fluctuations of the donor and acceptor fluorescence caused by Foerster resonance energy transfer (FRET) report on the conformational kinetics of the molecule. Here we assess the maximum likelihood-based analysis of donor and acceptor photon arrival trajectories as a method for extracting the conformational kinetics. Using computer generated data we quantify the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in selecting the true kinetic model. We find that the number of observed photons is the key parameter determining parameter estimation and model selection. For long trajectories, one can extract mean dwell times that are comparable to inter-photon times.

  9. Algorithms of maximum likelihood data clustering with applications

    Science.gov (United States)

    Giada, Lorenzo; Marsili, Matteo

    2002-12-01

    We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

  10. Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications

    International Nuclear Information System (INIS)

    Mungan, Muhittin; Ramasco, José J

    2010-01-01

    Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was generated by a certain grouping of the nodes and an expectation maximization algorithm is employed to infer that grouping. We show that the method admits further development to yield a stability analysis of the groupings that quantifies the extent to which each node influences its neighbors' group membership. Our approach naturally allows for the identification of the key elements responsible for the grouping and their resilience to changes in the network. Given the generality of the assumptions underlying the statistical model, such nodes are likely to play special roles in the original system. We illustrate this point by analyzing several empirical networks for which further information about the properties of the nodes is available. The search and identification of stabilizing nodes constitutes thus a novel technique to characterize the relevance of nodes in complex networks

  11. Multi-Channel Maximum Likelihood Pitch Estimation

    DEFF Research Database (Denmark)

    Christensen, Mads Græsbøll

    2012-01-01

    In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristics....... This essentially means that the model allows for different conditions in the various channels, like different signal-to-noise ratios, microphone characteristics and reverberation. Moreover, the method does not assume that a certain array structure is used but rather relies on a more general model and is hence...

  12. The unfolding of NaI(Tl) γ-ray spectrum based on maximum likelihood method

    International Nuclear Information System (INIS)

    Zhang Qingxian; Ge Liangquan; Gu Yi; Zeng Guoqiang; Lin Yanchang; Wang Guangxi

    2011-01-01

    NaI(Tl) detectors, having a good detection efficiency, are used to detect gamma rays in field surveys. But the poor energy resolution hinders their applications, despite the use of traditional methods to resolve the overlapped gamma-ray peaks. In this paper, the maximum likelihood (ML) solution is used to resolve the spectrum. The ML method,which is capable of decomposing the peaks in energy difference of over 2/3 FWHM, is applied to scale NaI(Tl) the spectrometer. The result shows that the net area is in proportion to the content of isotopes and the precision of scaling is better than the stripping ration method. (authors)

  13. Maximum likelihood estimation for integrated diffusion processes

    DEFF Research Database (Denmark)

    Baltazar-Larios, Fernando; Sørensen, Michael

    We propose a method for obtaining maximum likelihood estimates of parameters in diffusion models when the data is a discrete time sample of the integral of the process, while no direct observations of the process itself are available. The data are, moreover, assumed to be contaminated...... EM-algorithm to obtain maximum likelihood estimates of the parameters in the diffusion model. As part of the algorithm, we use a recent simple method for approximate simulation of diffusion bridges. In simulation studies for the Ornstein-Uhlenbeck process and the CIR process the proposed method works...... by measurement errors. Integrated volatility is an example of this type of observations. Another example is ice-core data on oxygen isotopes used to investigate paleo-temperatures. The data can be viewed as incomplete observations of a model with a tractable likelihood function. Therefore we propose a simulated...

  14. Semi-Parametric Maximum Likelihood Method for Interaction in Case-Mother Control-Mother Designs: Package SPmlficmcm

    Directory of Open Access Journals (Sweden)

    Moliere Nguile-Makao

    2015-12-01

    Full Text Available The analysis of interaction effects involving genetic variants and environmental exposures on the risk of adverse obstetric and early-life outcomes is generally performed using standard logistic regression in the case-mother and control-mother design. However such an analysis is inefficient because it does not take into account the natural family-based constraints present in the parent-child relationship. Recently, a new approach based on semi-parametric maximum likelihood estimation was proposed. The advantage of this approach is that it takes into account the parental relationship between the mother and her child in estimation. But a package implementing this method has not been widely available. In this paper, we present SPmlficmcm, an R package implementing this new method and we propose an extension of the method to handle missing offspring genotype data by maximum likelihood estimation. Our choice to treat missing data of the offspring genotype was motivated by the fact that in genetic association studies where the genetic data of mother and child are available, there are usually more missing data on the genotype of the offspring than that of the mother. The package builds a non-linear system from the data and solves and computes the estimates from the gradient and the Hessian matrix of the log profile semi-parametric likelihood function. Finally, we analyze a simulated dataset to show the usefulness of the package.

  15. Maximum likelihood estimation of the parameters of nonminimum phase and noncausal ARMA models

    DEFF Research Database (Denmark)

    Rasmussen, Klaus Bolding

    1994-01-01

    The well-known prediction-error-based maximum likelihood (PEML) method can only handle minimum phase ARMA models. This paper presents a new method known as the back-filtering-based maximum likelihood (BFML) method, which can handle nonminimum phase and noncausal ARMA models. The BFML method...... is identical to the PEML method in the case of a minimum phase ARMA model, and it turns out that the BFML method incorporates a noncausal ARMA filter with poles outside the unit circle for estimation of the parameters of a causal, nonminimum phase ARMA model...

  16. MXLKID: a maximum likelihood parameter identifier

    International Nuclear Information System (INIS)

    Gavel, D.T.

    1980-07-01

    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

  17. Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation

    International Nuclear Information System (INIS)

    Bardsley, Johnathan M; Goldes, John

    2009-01-01

    In image processing applications, image intensity is often measured via the counting of incident photons emitted by the object of interest. In such cases, image data noise is accurately modeled by a Poisson distribution. This motivates the use of Poisson maximum likelihood estimation for image reconstruction. However, when the underlying model equation is ill-posed, regularization is needed. Regularized Poisson likelihood estimation has been studied extensively by the authors, though a problem of high importance remains: the choice of the regularization parameter. We will present three statistically motivated methods for choosing the regularization parameter, and numerical examples will be presented to illustrate their effectiveness

  18. Cosmic shear measurement with maximum likelihood and maximum a posteriori inference

    Science.gov (United States)

    Hall, Alex; Taylor, Andy

    2017-06-01

    We investigate the problem of noise bias in maximum likelihood and maximum a posteriori estimators for cosmic shear. We derive the leading and next-to-leading order biases and compute them in the context of galaxy ellipticity measurements, extending previous work on maximum likelihood inference for weak lensing. We show that a large part of the bias on these point estimators can be removed using information already contained in the likelihood when a galaxy model is specified, without the need for external calibration. We test these bias-corrected estimators on simulated galaxy images similar to those expected from planned space-based weak lensing surveys, with promising results. We find that the introduction of an intrinsic shape prior can help with mitigation of noise bias, such that the maximum a posteriori estimate can be made less biased than the maximum likelihood estimate. Second-order terms offer a check on the convergence of the estimators, but are largely subdominant. We show how biases propagate to shear estimates, demonstrating in our simple set-up that shear biases can be reduced by orders of magnitude and potentially to within the requirements of planned space-based surveys at mild signal-to-noise ratio. We find that second-order terms can exhibit significant cancellations at low signal-to-noise ratio when Gaussian noise is assumed, which has implications for inferring the performance of shear-measurement algorithms from simplified simulations. We discuss the viability of our point estimators as tools for lensing inference, arguing that they allow for the robust measurement of ellipticity and shear.

  19. A theory of timing in scintillation counters based on maximum likelihood estimation

    International Nuclear Information System (INIS)

    Tomitani, Takehiro

    1982-01-01

    A theory of timing in scintillation counters based on the maximum likelihood estimation is presented. An optimum filter that minimizes the variance of timing is described. A simple formula to estimate the variance of timing is presented as a function of photoelectron number, scintillation decay constant and the single electron transit time spread in the photomultiplier. The present method was compared with the theory by E. Gatti and V. Svelto. The proposed method was applied to two simple models and rough estimations of potential time resolution of several scintillators are given. The proposed method is applicable to the timing in Cerenkov counters and semiconductor detectors as well. (author)

  20. Maximum Likelihood Blind Channel Estimation for Space-Time Coding Systems

    Directory of Open Access Journals (Sweden)

    Hakan A. Çırpan

    2002-05-01

    Full Text Available Sophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems. In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional receive diversity in order to provide diversity at the receiver and coding gain without sacrificing the bandwidth. In this paper, we consider the problem of blind estimation of space-time coded signals along with the channel parameters. Both conditional and unconditional maximum likelihood approaches are developed and iterative solutions are proposed. The conditional maximum likelihood algorithm is based on iterative least squares with projection whereas the unconditional maximum likelihood approach is developed by means of finite state Markov process modelling. The performance analysis issues of the proposed methods are studied. Finally, some simulation results are presented.

  1. Approximate maximum likelihood estimation for population genetic inference.

    Science.gov (United States)

    Bertl, Johanna; Ewing, Gregory; Kosiol, Carolin; Futschik, Andreas

    2017-11-27

    In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.

  2. Maximum likelihood as a common computational framework in tomotherapy

    International Nuclear Information System (INIS)

    Olivera, G.H.; Shepard, D.M.; Reckwerdt, P.J.; Ruchala, K.; Zachman, J.; Fitchard, E.E.; Mackie, T.R.

    1998-01-01

    Tomotherapy is a dose delivery technique using helical or axial intensity modulated beams. One of the strengths of the tomotherapy concept is that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, dose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these processes would be very appealing. The maximum likelihood estimator, originally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose reconstruction and kilovoltage and/or megavoltage image reconstruction. These processes involve computations that require comparable physical methods. They are also based on equivalent assumptions, and they have similar mathematical solutions. As a result, the maximum likelihood approach is able to provide a common framework for all three of these computational problems. We will demonstrate how maximum likelihood methods can be applied to optimization planning, dose reconstruction and megavoltage image reconstruction in tomotherapy. Results for planning optimization, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed. Future directions for this work are also suggested. (author)

  3. An Efficient UD-Based Algorithm for the Computation of Maximum Likelihood Sensitivity of Continuous-Discrete Systems

    DEFF Research Database (Denmark)

    Boiroux, Dimitri; Juhl, Rune; Madsen, Henrik

    2016-01-01

    This paper addresses maximum likelihood parameter estimation of continuous-time nonlinear systems with discrete-time measurements. We derive an efficient algorithm for the computation of the log-likelihood function and its gradient, which can be used in gradient-based optimization algorithms....... This algorithm uses UD decomposition of symmetric matrices and the array algorithm for covariance update and gradient computation. We test our algorithm on the Lotka-Volterra equations. Compared to the maximum likelihood estimation based on finite difference gradient computation, we get a significant speedup...

  4. Maximum Likelihood Approach for RFID Tag Set Cardinality Estimation with Detection Errors

    DEFF Research Database (Denmark)

    Nguyen, Chuyen T.; Hayashi, Kazunori; Kaneko, Megumi

    2013-01-01

    Abstract Estimation schemes of Radio Frequency IDentification (RFID) tag set cardinality are studied in this paper using Maximum Likelihood (ML) approach. We consider the estimation problem under the model of multiple independent reader sessions with detection errors due to unreliable radio...... is evaluated under dierent system parameters and compared with that of the conventional method via computer simulations assuming flat Rayleigh fading environments and framed-slotted ALOHA based protocol. Keywords RFID tag cardinality estimation maximum likelihood detection error...

  5. Preliminary application of maximum likelihood method in HL-2A Thomson scattering system

    International Nuclear Information System (INIS)

    Yao Ke; Huang Yuan; Feng Zhen; Liu Chunhua; Li Enping; Nie Lin

    2010-01-01

    Maximum likelihood method to process the data of HL-2A Thomson scattering system is presented. Using mathematical statistics, this method maximizes the possibility of the likeness between the theoretical data and the observed data, so that we could get more accurate result. It has been proved to be applicable in comparison with that of the ratios method, and some of the drawbacks in ratios method do not exist in this new one. (authors)

  6. APPLICATION OF A GENERALIZED MAXIMUM LIKELIHOOD METHOD IN THE REDUCTION OF MULTICOMPONENT LIQUID-LIQUID EQUILIBRIUM DATA

    Directory of Open Access Journals (Sweden)

    L. STRAGEVITCH

    1997-03-01

    Full Text Available The equations of the method based on the maximum likelihood principle have been rewritten in a suitable generalized form to allow the use of any number of implicit constraints in the determination of model parameters from experimental data and from the associated experimental uncertainties. In addition to the use of any number of constraints, this method also allows data, with different numbers of constraints, to be reduced simultaneously. Application of the method is illustrated in the reduction of liquid-liquid equilibrium data of binary, ternary and quaternary systems simultaneously

  7. Maximum likelihood estimation of finite mixture model for economic data

    Science.gov (United States)

    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.

  8. Maximum-Likelihood Detection Of Noncoherent CPM

    Science.gov (United States)

    Divsalar, Dariush; Simon, Marvin K.

    1993-01-01

    Simplified detectors proposed for use in maximum-likelihood-sequence detection of symbols in alphabet of size M transmitted by uncoded, full-response continuous phase modulation over radio channel with additive white Gaussian noise. Structures of receivers derived from particular interpretation of maximum-likelihood metrics. Receivers include front ends, structures of which depends only on M, analogous to those in receivers of coherent CPM. Parts of receivers following front ends have structures, complexity of which would depend on N.

  9. Superfast maximum-likelihood reconstruction for quantum tomography

    Science.gov (United States)

    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.

  10. Application of the method of maximum likelihood to the determination of cepheid radii

    International Nuclear Information System (INIS)

    Balona, L.A.

    1977-01-01

    A method is described whereby the radius of any pulsating star can be obtained by applying the Principle of Maximum Likelihood. The relative merits of this method and of the usual Baade-Wesselink method are discussed in an Appendix. The new method is applied to 54 well-observed cepheids which include a number of spectroscopic binaries and two W Vir stars. An empirical period-radius relation is constructed and discussed in terms of two recent period-luminosity-colour calibrations. It is shown that the new method gives radii with an error of no more than 10 per cent. (author)

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

    Science.gov (United States)

    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/.

  12. Finite mixture model: A maximum likelihood estimation approach on time series data

    Science.gov (United States)

    Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

    2014-09-01

    Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

  13. Maximum Likelihood Estimation and Inference With Examples in R, SAS and ADMB

    CERN Document Server

    Millar, Russell B

    2011-01-01

    This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statis

  14. Neutron spectra unfolding with maximum entropy and maximum likelihood

    International Nuclear Information System (INIS)

    Itoh, Shikoh; Tsunoda, Toshiharu

    1989-01-01

    A new unfolding theory has been established on the basis of the maximum entropy principle and the maximum likelihood method. This theory correctly embodies the Poisson statistics of neutron detection, and always brings a positive solution over the whole energy range. Moreover, the theory unifies both problems of overdetermined and of underdetermined. For the latter, the ambiguity in assigning a prior probability, i.e. the initial guess in the Bayesian sense, has become extinct by virtue of the principle. An approximate expression of the covariance matrix for the resultant spectra is also presented. An efficient algorithm to solve the nonlinear system, which appears in the present study, has been established. Results of computer simulation showed the effectiveness of the present theory. (author)

  15. Approximate maximum parsimony and ancestral maximum likelihood.

    Science.gov (United States)

    Alon, Noga; Chor, Benny; Pardi, Fabio; Rapoport, Anat

    2010-01-01

    We explore the maximum parsimony (MP) and ancestral maximum likelihood (AML) criteria in phylogenetic tree reconstruction. Both problems are NP-hard, so we seek approximate solutions. We formulate the two problems as Steiner tree problems under appropriate distances. The gist of our approach is the succinct characterization of Steiner trees for a small number of leaves for the two distances. This enables the use of known Steiner tree approximation algorithms. The approach leads to a 16/9 approximation ratio for AML and asymptotically to a 1.55 approximation ratio for MP.

  16. Optimized Large-scale CMB Likelihood and Quadratic Maximum Likelihood Power Spectrum Estimation

    Science.gov (United States)

    Gjerløw, E.; Colombo, L. P. L.; Eriksen, H. K.; Górski, K. M.; Gruppuso, A.; Jewell, J. B.; Plaszczynski, S.; Wehus, I. K.

    2015-11-01

    We revisit the problem of exact cosmic microwave background (CMB) likelihood and power spectrum estimation with the goal of minimizing computational costs through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al., and here we develop it into a fully functioning computational framework for large-scale polarization analysis, adopting WMAP as a working example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors, and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at ℓ ≤ 32 and a maximum shift in the mean values of a joint distribution of an amplitude-tilt model of 0.006σ. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation, which requires less than 3 GB of memory and 2 CPU minutes per iteration for ℓ ≤ 32, rendering low-ℓ QML CMB power spectrum analysis fully tractable on a standard laptop.

  17. Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik

    2017-01-01

    The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...

  18. Maximum likelihood versus likelihood-free quantum system identification in the atom maser

    International Nuclear Information System (INIS)

    Catana, Catalin; Kypraios, Theodore; Guţă, Mădălin

    2014-01-01

    We consider the problem of estimating a dynamical parameter of a Markovian quantum open system (the atom maser), by performing continuous time measurements in the system's output (outgoing atoms). Two estimation methods are investigated and compared. Firstly, the maximum likelihood estimator (MLE) takes into account the full measurement data and is asymptotically optimal in terms of its mean square error. Secondly, the ‘likelihood-free’ method of approximate Bayesian computation (ABC) produces an approximation of the posterior distribution for a given set of summary statistics, by sampling trajectories at different parameter values and comparing them with the measurement data via chosen statistics. Building on previous results which showed that atom counts are poor statistics for certain values of the Rabi angle, we apply MLE to the full measurement data and estimate its Fisher information. We then select several correlation statistics such as waiting times, distribution of successive identical detections, and use them as input of the ABC algorithm. The resulting posterior distribution follows closely the data likelihood, showing that the selected statistics capture ‘most’ statistical information about the Rabi angle. (paper)

  19. Maximum likelihood positioning for gamma-ray imaging detectors with depth of interaction measurement

    International Nuclear Information System (INIS)

    Lerche, Ch.W.; Ros, A.; Monzo, J.M.; Aliaga, R.J.; Ferrando, N.; Martinez, J.D.; Herrero, V.; Esteve, R.; Gadea, R.; Colom, R.J.; Toledo, J.; Mateo, F.; Sebastia, A.; Sanchez, F.; Benlloch, J.M.

    2009-01-01

    The center of gravity algorithm leads to strong artifacts for gamma-ray imaging detectors that are based on monolithic scintillation crystals and position sensitive photo-detectors. This is a consequence of using the centroids as position estimates. The fact that charge division circuits can also be used to compute the standard deviation of the scintillation light distribution opens a way out of this drawback. We studied the feasibility of maximum likelihood estimation for computing the true gamma-ray photo-conversion position from the centroids and the standard deviation of the light distribution. The method was evaluated on a test detector that consists of the position sensitive photomultiplier tube H8500 and a monolithic LSO crystal (42mmx42mmx10mm). Spatial resolution was measured for the centroids and the maximum likelihood estimates. The results suggest that the maximum likelihood positioning is feasible and partially removes the strong artifacts of the center of gravity algorithm.

  20. Maximum likelihood positioning for gamma-ray imaging detectors with depth of interaction measurement

    Energy Technology Data Exchange (ETDEWEB)

    Lerche, Ch.W. [Grupo de Sistemas Digitales, ITACA, Universidad Politecnica de Valencia, 46022 Valencia (Spain)], E-mail: lerche@ific.uv.es; Ros, A. [Grupo de Fisica Medica Nuclear, IFIC, Universidad de Valencia-Consejo Superior de Investigaciones Cientificas, 46980 Paterna (Spain); Monzo, J.M.; Aliaga, R.J.; Ferrando, N.; Martinez, J.D.; Herrero, V.; Esteve, R.; Gadea, R.; Colom, R.J.; Toledo, J.; Mateo, F.; Sebastia, A. [Grupo de Sistemas Digitales, ITACA, Universidad Politecnica de Valencia, 46022 Valencia (Spain); Sanchez, F.; Benlloch, J.M. [Grupo de Fisica Medica Nuclear, IFIC, Universidad de Valencia-Consejo Superior de Investigaciones Cientificas, 46980 Paterna (Spain)

    2009-06-01

    The center of gravity algorithm leads to strong artifacts for gamma-ray imaging detectors that are based on monolithic scintillation crystals and position sensitive photo-detectors. This is a consequence of using the centroids as position estimates. The fact that charge division circuits can also be used to compute the standard deviation of the scintillation light distribution opens a way out of this drawback. We studied the feasibility of maximum likelihood estimation for computing the true gamma-ray photo-conversion position from the centroids and the standard deviation of the light distribution. The method was evaluated on a test detector that consists of the position sensitive photomultiplier tube H8500 and a monolithic LSO crystal (42mmx42mmx10mm). Spatial resolution was measured for the centroids and the maximum likelihood estimates. The results suggest that the maximum likelihood positioning is feasible and partially removes the strong artifacts of the center of gravity algorithm.

  1. Evolutionary analysis of apolipoprotein E by Maximum Likelihood and complex network methods

    Directory of Open Access Journals (Sweden)

    Leandro de Jesus Benevides

    Full Text Available Abstract Apolipoprotein E (apo E is a human glycoprotein with 299 amino acids, and it is a major component of very low density lipoproteins (VLDL and a group of high-density lipoproteins (HDL. Phylogenetic studies are important to clarify how various apo E proteins are related in groups of organisms and whether they evolved from a common ancestor. Here, we aimed at performing a phylogenetic study on apo E carrying organisms. We employed a classical and robust method, such as Maximum Likelihood (ML, and compared the results using a more recent approach based on complex networks. Thirty-two apo E amino acid sequences were downloaded from NCBI. A clear separation could be observed among three major groups: mammals, fish and amphibians. The results obtained from ML method, as well as from the constructed networks showed two different groups: one with mammals only (C1 and another with fish (C2, and a single node with the single sequence available for an amphibian. The accordance in results from the different methods shows that the complex networks approach is effective in phylogenetic studies. Furthermore, our results revealed the conservation of apo E among animal groups.

  2. Analysis of Minute Features in Speckled Imagery with Maximum Likelihood Estimation

    Directory of Open Access Journals (Sweden)

    Alejandro C. Frery

    2004-12-01

    Full Text Available This paper deals with numerical problems arising when performing maximum likelihood parameter estimation in speckled imagery using small samples. The noise that appears in images obtained with coherent illumination, as is the case of sonar, laser, ultrasound-B, and synthetic aperture radar, is called speckle, and it can neither be assumed Gaussian nor additive. The properties of speckle noise are well described by the multiplicative model, a statistical framework from which stem several important distributions. Amongst these distributions, one is regarded as the universal model for speckled data, namely, the 𝒢0 law. This paper deals with amplitude data, so the 𝒢A0 distribution will be used. The literature reports that techniques for obtaining estimates (maximum likelihood, based on moments and on order statistics of the parameters of the 𝒢A0 distribution require samples of hundreds, even thousands, of observations in order to obtain sensible values. This is verified for maximum likelihood estimation, and a proposal based on alternate optimization is made to alleviate this situation. The proposal is assessed with real and simulated data, showing that the convergence problems are no longer present. A Monte Carlo experiment is devised to estimate the quality of maximum likelihood estimators in small samples, and real data is successfully analyzed with the proposed alternated procedure. Stylized empirical influence functions are computed and used to choose a strategy for computing maximum likelihood estimates that is resistant to outliers.

  3. Maximum Likelihood Compton Polarimetry with the Compton Spectrometer and Imager

    Energy Technology Data Exchange (ETDEWEB)

    Lowell, A. W.; Boggs, S. E; Chiu, C. L.; Kierans, C. A.; Sleator, C.; Tomsick, J. A.; Zoglauer, A. C. [Space Sciences Laboratory, University of California, Berkeley (United States); Chang, H.-K.; Tseng, C.-H.; Yang, C.-Y. [Institute of Astronomy, National Tsing Hua University, Taiwan (China); Jean, P.; Ballmoos, P. von [IRAP Toulouse (France); Lin, C.-H. [Institute of Physics, Academia Sinica, Taiwan (China); Amman, M. [Lawrence Berkeley National Laboratory (United States)

    2017-10-20

    Astrophysical polarization measurements in the soft gamma-ray band are becoming more feasible as detectors with high position and energy resolution are deployed. Previous work has shown that the minimum detectable polarization (MDP) of an ideal Compton polarimeter can be improved by ∼21% when an unbinned, maximum likelihood method (MLM) is used instead of the standard approach of fitting a sinusoid to a histogram of azimuthal scattering angles. Here we outline a procedure for implementing this maximum likelihood approach for real, nonideal polarimeters. As an example, we use the recent observation of GRB 160530A with the Compton Spectrometer and Imager. We find that the MDP for this observation is reduced by 20% when the MLM is used instead of the standard method.

  4. A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

    Directory of Open Access Journals (Sweden)

    Edwards Scott V

    2010-10-01

    Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the

  5. Maximum-likelihood estimation of the hyperbolic parameters from grouped observations

    DEFF Research Database (Denmark)

    Jensen, Jens Ledet

    1988-01-01

    a least-squares problem. The second procedure Hypesti first approaches the maximum-likelihood estimate by iterating in the profile-log likelihood function for the scale parameter. Close to the maximum of the likelihood function, the estimation is brought to an end by iteration, using all four parameters...

  6. Design of simplified maximum-likelihood receivers for multiuser CPM systems.

    Science.gov (United States)

    Bing, Li; Bai, Baoming

    2014-01-01

    A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser receivers with significantly (even exponentially in some cases) reduced complexity and marginal performance degradation.

  7. Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  9. Estimation of Financial Agent-Based Models with Simulated Maximum Likelihood

    Czech Academy of Sciences Publication Activity Database

    Kukačka, Jiří; Baruník, Jozef

    2017-01-01

    Roč. 85, č. 1 (2017), s. 21-45 ISSN 0165-1889 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : heterogeneous agent model, * simulated maximum likelihood * switching Subject RIV: AH - Economics OBOR OECD: Finance Impact factor: 1.000, year: 2016 http://library.utia.cas.cz/separaty/2017/E/kukacka-0478481.pdf

  10. THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.

    Science.gov (United States)

    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.

  11. Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties

    International Nuclear Information System (INIS)

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

    1996-06-01

    The fitting of data by χ 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 estimates for the fit parameters. They compare this method with a χ 2 -minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than ∼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

  12. LASER: A Maximum Likelihood Toolkit for Detecting Temporal Shifts in Diversification Rates From Molecular Phylogenies

    Directory of Open Access Journals (Sweden)

    Daniel L. Rabosky

    2006-01-01

    Full Text Available Rates of species origination and extinction can vary over time during evolutionary radiations, and it is possible to reconstruct the history of diversification using molecular phylogenies of extant taxa only. Maximum likelihood methods provide a useful framework for inferring temporal variation in diversification rates. LASER is a package for the R programming environment that implements maximum likelihood methods based on the birth-death process to test whether diversification rates have changed over time. LASER contrasts the likelihood of phylogenetic data under models where diversification rates have changed over time to alternative models where rates have remained constant over time. Major strengths of the package include the ability to detect temporal increases in diversification rates and the inference of diversification parameters under multiple rate-variable models of diversification. The program and associated documentation are freely available from the R package archive at http://cran.r-project.org.

  13. Design of Simplified Maximum-Likelihood Receivers for Multiuser CPM Systems

    Directory of Open Access Journals (Sweden)

    Li Bing

    2014-01-01

    Full Text Available A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser receivers with significantly (even exponentially in some cases reduced complexity and marginal performance degradation.

  14. Constructing valid density matrices on an NMR quantum information processor via maximum likelihood estimation

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Harpreet; Arvind; Dorai, Kavita, E-mail: kavita@iisermohali.ac.in

    2016-09-07

    Estimation of quantum states is an important step in any quantum information processing experiment. A naive reconstruction of the density matrix from experimental measurements can often give density matrices which are not positive, and hence not physically acceptable. How do we ensure that at all stages of reconstruction, we keep the density matrix positive? Recently a method has been suggested based on maximum likelihood estimation, wherein the density matrix is guaranteed to be positive definite. We experimentally implement this protocol on an NMR quantum information processor. We discuss several examples and compare with the standard method of state estimation. - Highlights: • State estimation using maximum likelihood method was performed on an NMR quantum information processor. • Physically valid density matrices were obtained every time in contrast to standard quantum state tomography. • Density matrices of several different entangled and separable states were reconstructed for two and three qubits.

  15. Targeted maximum likelihood estimation for a binary treatment: A tutorial.

    Science.gov (United States)

    Luque-Fernandez, Miguel Angel; Schomaker, Michael; Rachet, Bernard; Schnitzer, Mireille E

    2018-04-23

    When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine-learning methods. It therefore requires weaker assumptions than its competitors. We provide a step-by-step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R-code is provided in easy-to-read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the Appendix S1 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial. © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

  16. Narrow band interference cancelation in OFDM: Astructured maximum likelihood approach

    KAUST Repository

    Sohail, Muhammad Sadiq

    2012-06-01

    This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing (ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous with the frequency grid of the ZP-OFDM system. The proposed structure based technique uses the fact that the NBI signal is sparse as compared to the ZP-OFDM signal in the frequency domain. The structure is also useful in reducing the computational complexity of the proposed method. The paper also presents a data aided approach for improved NBI estimation. The suitability of the proposed method is demonstrated through simulations. © 2012 IEEE.

  17. A maximum likelihood framework for protein design

    Directory of Open Access Journals (Sweden)

    Philippe Hervé

    2006-06-01

    Full Text Available Abstract Background The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. Results We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered. Conclusion Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces

  18. THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM

    Directory of Open Access Journals (Sweden)

    Lúcio CARDOZO-FILHO

    1997-12-01

    Full Text Available The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.Foi empregado o método da máxima verossimilhança generalizado para determinação de parâmetros de interação binária entre os componentes do óleo essencial de laranja e dióxido de carbono. Foram usados dados experimentais de equilíbrio líquido-vapor de misturas binárias de dióxido de carbono e componentes do óleo essencial de laranja. O equilíbrio líquido-vapor foi modelado com as equações de Peng-Robinson e de Soave-Redlich-Kwong usando a metodologia proposta em 1979 por Asselineau, Bogdanic e Vidal. A escolha destes sistemas teve como objetivo demonstrar que o método da máxima verosimilhança produz parâmetros de interação binária, para equações cúbicas de estado capazes de descrever satisfatoriamente até mesmo o equilíbrio para o binário etanol/CO2. Os resultados comprovam que tanto a equação de Peng-Robinson quanto a de Soave-Redlich-Kwong podem ser empregadas para descrever o equilíbrio de fases para o sistemas: componentes do óleo essencial de laranja/CO2.

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

    Science.gov (United States)

    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.

  20. Elemental composition of cosmic rays using a maximum likelihood method

    International Nuclear Information System (INIS)

    Ruddick, K.

    1996-01-01

    We present a progress report on our attempts to determine the composition of cosmic rays in the knee region of the energy spectrum. We have used three different devices to measure properties of the extensive air showers produced by primary cosmic rays: the Soudan 2 underground detector measures the muon flux deep underground, a proportional tube array samples shower density at the surface of the earth, and a Cherenkov array observes light produced high in the atmosphere. We have begun maximum likelihood fits to these measurements with the hope of determining the nuclear mass number A on an event by event basis. (orig.)

  1. Maximum Likelihood Method for Predicting Environmental Conditions from Assemblage Composition: The R Package bio.infer

    Directory of Open Access Journals (Sweden)

    Lester L. Yuan

    2007-06-01

    Full Text Available This paper provides a brief introduction to the R package bio.infer, a set of scripts that facilitates the use of maximum likelihood (ML methods for predicting environmental conditions from assemblage composition. Environmental conditions can often be inferred from only biological data, and these inferences are useful when other sources of data are unavailable. ML prediction methods are statistically rigorous and applicable to a broader set of problems than more commonly used weighted averaging techniques. However, ML methods require a substantially greater investment of time to program algorithms and to perform computations. This package is designed to reduce the effort required to apply ML prediction methods.

  2. Application of the Method of Maximum Likelihood to Identification of Bipedal Walking Robots

    Czech Academy of Sciences Publication Activity Database

    Dolinský, Kamil; Čelikovský, Sergej

    (2017) ISSN 1063-6536 R&D Projects: GA ČR(CZ) GA17-04682S Institutional support: RVO:67985556 Keywords : Control * identification * maximum likelihood (ML) * walking robots Subject RIV: BC - Control Systems Theory Impact factor: 3.882, year: 2016 http://ieeexplore.ieee.org/document/7954032/

  3. Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods

    International Nuclear Information System (INIS)

    Rajan, Jeny; Jeurissen, Ben; Sijbers, Jan; Verhoye, Marleen; Van Audekerke, Johan

    2011-01-01

    In this paper, we propose a method to denoise magnitude magnetic resonance (MR) images, which are Rician distributed. Conventionally, maximum likelihood methods incorporate the Rice distribution to estimate the true, underlying signal from a local neighborhood within which the signal is assumed to be constant. However, if this assumption is not met, such filtering will lead to blurred edges and loss of fine structures. As a solution to this problem, we put forward the concept of restricted local neighborhoods where the true intensity for each noisy pixel is estimated from a set of preselected neighboring pixels. To this end, a reference image is created from the noisy image using a recently proposed nonlocal means algorithm. This reference image is used as a prior for further noise reduction. A scheme is developed to locally select an appropriate subset of pixels from which the underlying signal is estimated. Experimental results based on the peak signal to noise ratio, structural similarity index matrix, Bhattacharyya coefficient and mean absolute difference from synthetic and real MR images demonstrate the superior performance of the proposed method over other state-of-the-art methods.

  4. The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.

    Directory of Open Access Journals (Sweden)

    Ross S Williamson

    2015-04-01

    Full Text Available 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.

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

    DEFF Research Database (Denmark)

    Scheike, Thomas Harder; Juul, Anders

    2004-01-01

    -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......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...

  6. Bayesian interpretation of Generalized empirical likelihood by maximum entropy

    OpenAIRE

    Rochet , Paul

    2011-01-01

    We study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian approach to the problem can be adopted, extending the MEM procedure to parametric moment conditions. We show in particular that a large number of GEL estimators can be interpreted as a maximum entropy solution. Moreover, we provide a more general field of applications by proving the method to be rob...

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

    Science.gov (United States)

    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.

  8. MAXIMUM LIKELIHOOD CLASSIFICATION OF HIGH-RESOLUTION SAR IMAGES IN URBAN AREA

    Directory of Open Access Journals (Sweden)

    M. Soheili Majd

    2012-09-01

    Full Text Available In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, log- normal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM, maximum-likelihood (ML methodology, and log-cumulants method (MoML. Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum–Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.

  9. Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models

    NARCIS (Netherlands)

    Mesters, G.; Koopman, S.J.; Ooms, M.

    2016-01-01

    An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Dongming Li

    2017-04-01

    Full Text Available 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.

  12. Estimation of flashover voltage probability of overhead line insulators under industrial pollution, based on maximum likelihood method

    International Nuclear Information System (INIS)

    Arab, M.N.; Ayaz, M.

    2004-01-01

    The performance of transmission line insulator is greatly affected by dust, fumes from industrial areas and saline deposit near the coast. Such pollutants in the presence of moisture form a coating on the surface of the insulator, which in turn allows the passage of leakage current. This leakage builds up to a point where flashover develops. The flashover is often followed by permanent failure of insulation resulting in prolong outages. With the increase in system voltage owing to the greater demand of electrical energy over the past few decades, the importance of flashover due to pollution has received special attention. The objective of the present work was to study the performance of overhead line insulators in the presence of contaminants such as induced salts. A detailed review of the literature and the mechanisms of insulator flashover due to the pollution are presented. Experimental investigations on the behavior of overhead line insulators under industrial salt contamination are carried out. A special fog chamber was designed in which the contamination testing of insulators was carried out. Flashover behavior under various degrees of contamination of insulators with the most common industrial fume components such as Nitrate and Sulphate compounds was studied. Substituting the normal distribution parameter in the probability distribution function based on maximum likelihood develops a statistical method. The method gives a high accuracy in the estimation of the 50% flashover voltage, which is then used to evaluate the critical flashover index at various contamination levels. The critical flashover index is a valuable parameter in insulation design for numerous applications. (author)

  13. Penalized Maximum Likelihood Estimation for univariate normal mixture distributions

    International Nuclear Information System (INIS)

    Ridolfi, A.; Idier, J.

    2001-01-01

    Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM algorithm is derived, which is still explicit and which intrinsically assures that the estimates are not singular. Numerical evidence of the latter property is put forward with a test

  14. Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method

    Directory of Open Access Journals (Sweden)

    Sonali Sachin Sankpal

    2016-01-01

    Full Text Available Scattering and absorption of light is main reason for limited visibility in water. The suspended particles and dissolved chemical compounds in water are also responsible for scattering and absorption of light in water. The limited visibility in water results in degradation of underwater images. The visibility can be increased by using artificial light source in underwater imaging system. But the artificial light illuminates the scene in a nonuniform fashion. It produces bright spot at the center with the dark region at surroundings. In some cases imaging system itself creates dark region in the image by producing shadow on the objects. The problem of nonuniform illumination is neglected by the researchers in most of the image enhancement techniques of underwater images. Also very few methods are discussed showing the results on color images. This paper suggests a method for nonuniform illumination correction for underwater images. The method assumes that natural underwater images are Rayleigh distributed. This paper used maximum likelihood estimation of scale parameter to map distribution of image to Rayleigh distribution. The method is compared with traditional methods for nonuniform illumination correction using no-reference image quality metrics like average luminance, average information entropy, normalized neighborhood function, average contrast, and comprehensive assessment function.

  15. Maximum likelihood pixel labeling using a spatially variant finite mixture model

    International Nuclear Information System (INIS)

    Gopal, S.S.; Hebert, T.J.

    1996-01-01

    We propose a spatially-variant mixture model for pixel labeling. Based on this spatially-variant mixture model we derive an expectation maximization algorithm for maximum likelihood estimation of the pixel labels. While most algorithms using mixture models entail the subsequent use of a Bayes classifier for pixel labeling, the proposed algorithm yields maximum likelihood estimates of the labels themselves and results in unambiguous pixel labels. The proposed algorithm is fast, robust, easy to implement, flexible in that it can be applied to any arbitrary image data where the number of classes is known and, most importantly, obviates the need for an explicit labeling rule. The algorithm is evaluated both quantitatively and qualitatively on simulated data and on clinical magnetic resonance images of the human brain

  16. A short proof that phylogenetic tree reconstruction by maximum likelihood is hard.

    Science.gov (United States)

    Roch, Sebastien

    2006-01-01

    Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.

  17. A Short Proof that Phylogenetic Tree Reconstruction by Maximum Likelihood is Hard

    OpenAIRE

    Roch, S.

    2005-01-01

    Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.

  18. Parallelization of maximum likelihood fits with OpenMP and CUDA

    CERN Document Server

    Jarp, S; Leduc, J; Nowak, A; Pantaleo, F

    2011-01-01

    Data analyses based on maximum likelihood fits are commonly used in the high energy physics community for fitting statistical models to data samples. This technique requires the numerical minimization of the negative log-likelihood function. MINUIT is the most common package used for this purpose in the high energy physics community. The main algorithm in this package, MIGRAD, searches the minimum by using the gradient information. The procedure requires several evaluations of the function, depending on the number of free parameters and their initial values. The whole procedure can be very CPU-time consuming in case of complex functions, with several free parameters, many independent variables and large data samples. Therefore, it becomes particularly important to speed-up the evaluation of the negative log-likelihood function. In this paper we present an algorithm and its implementation which benefits from data vectorization and parallelization (based on OpenMP) and which was also ported to Graphics Processi...

  19. Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise

    DEFF Research Database (Denmark)

    Kuklasinski, Adam; Doclo, Simon; Jensen, Søren Holdt

    2016-01-01

    In this contribution we focus on the problem of power spectral density (PSD) estimation from multiple microphone signals in reverberant and noisy environments. The PSD estimation method proposed in this paper is based on the maximum likelihood (ML) methodology. In particular, we derive a novel ML...... instrumental measures and is shown to be higher than when the competing estimator is used. Moreover, we perform a speech intelligibility test where we demonstrate that both the proposed and the competing PSD estimators lead to similar intelligibility improvements......., it is shown numerically that the mean squared estimation error achieved by the proposed method is near the limit set by the corresponding Cram´er-Rao lower bound. The speech dereverberation performance of a multi-channel Wiener filter (MWF) based on the proposed PSD estimators is measured using several...

  20. Maximum likelihood estimation of ancestral codon usage bias parameters in Drosophila

    DEFF Research Database (Denmark)

    Nielsen, Rasmus; Bauer DuMont, Vanessa L; Hubisz, Melissa J

    2007-01-01

    : the selection coefficient for optimal codon usage (S), allowing joint maximum likelihood estimation of S and the dN/dS ratio. We apply the method to previously published data from Drosophila melanogaster, Drosophila simulans, and Drosophila yakuba and show, in accordance with previous results, that the D...

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

    Science.gov (United States)

    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…

  2. Modelling maximum likelihood estimation of availability

    International Nuclear Information System (INIS)

    Waller, R.A.; Tietjen, G.L.; Rock, G.W.

    1975-01-01

    Suppose the performance of a nuclear powered electrical generating power plant is continuously monitored to record the sequence of failure and repairs during sustained operation. The purpose of this study is to assess one method of estimating the performance of the power plant when the measure of performance is availability. That is, we determine the probability that the plant is operational at time t. To study the availability of a power plant, we first assume statistical models for the variables, X and Y, which denote the time-to-failure and the time-to-repair variables, respectively. Once those statistical models are specified, the availability, A(t), can be expressed as a function of some or all of their parameters. Usually those parameters are unknown in practice and so A(t) is unknown. This paper discusses the maximum likelihood estimator of A(t) when the time-to-failure model for X is an exponential density with parameter, lambda, and the time-to-repair model for Y is an exponential density with parameter, theta. Under the assumption of exponential models for X and Y, it follows that the instantaneous availability at time t is A(t)=lambda/(lambda+theta)+theta/(lambda+theta)exp[-[(1/lambda)+(1/theta)]t] with t>0. Also, the steady-state availability is A(infinity)=lambda/(lambda+theta). We use the observations from n failure-repair cycles of the power plant, say X 1 , X 2 , ..., Xsub(n), Y 1 , Y 2 , ..., Ysub(n) to present the maximum likelihood estimators of A(t) and A(infinity). The exact sampling distributions for those estimators and some statistical properties are discussed before a simulation model is used to determine 95% simulation intervals for A(t). The methodology is applied to two examples which approximate the operating history of two nuclear power plants. (author)

  3. Maximum Simulated Likelihood and Expectation-Maximization Methods to Estimate Random Coefficients Logit with Panel Data

    DEFF Research Database (Denmark)

    Cherchi, Elisabetta; Guevara, Cristian

    2012-01-01

    with cross-sectional or with panel data, and (d) EM systematically attained more efficient estimators than the MSL method. The results imply that if the purpose of the estimation is only to determine the ratios of the model parameters (e.g., the value of time), the EM method should be preferred. For all......The random coefficients logit model allows a more realistic representation of agents' behavior. However, the estimation of that model may involve simulation, which may become impractical with many random coefficients because of the curse of dimensionality. In this paper, the traditional maximum...... simulated likelihood (MSL) method is compared with the alternative expectation- maximization (EM) method, which does not require simulation. Previous literature had shown that for cross-sectional data, MSL outperforms the EM method in the ability to recover the true parameters and estimation time...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  6. Maximum likelihood based multi-channel isotropic reverberation reduction for hearing aids

    DEFF Research Database (Denmark)

    Kuklasiński, Adam; Doclo, Simon; Jensen, Søren Holdt

    2014-01-01

    We propose a multi-channel Wiener filter for speech dereverberation in hearing aids. The proposed algorithm uses joint maximum likelihood estimation of the speech and late reverberation spectral variances, under the assumption that the late reverberant sound field is cylindrically isotropic....... The dereverberation performance of the algorithm is evaluated using computer simulations with realistic hearing aid microphone signals including head-related effects. The algorithm is shown to work well with signals reverberated both by synthetic and by measured room impulse responses, achieving improvements...

  7. Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

    International Nuclear Information System (INIS)

    Bozchalooi, I Soltani; Liang, Ming

    2009-01-01

    Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings

  8. Phylogenetic analysis using parsimony and likelihood methods.

    Science.gov (United States)

    Yang, Z

    1996-02-01

    The assumptions underlying the maximum-parsimony (MP) method of phylogenetic tree reconstruction were intuitively examined by studying the way the method works. Computer simulations were performed to corroborate the intuitive examination. Parsimony appears to involve very stringent assumptions concerning the process of sequence evolution, such as constancy of substitution rates between nucleotides, constancy of rates across nucleotide sites, and equal branch lengths in the tree. For practical data analysis, the requirement of equal branch lengths means similar substitution rates among lineages (the existence of an approximate molecular clock), relatively long interior branches, and also few species in the data. However, a small amount of evolution is neither a necessary nor a sufficient requirement of the method. The difficulties involved in the application of current statistical estimation theory to tree reconstruction were discussed, and it was suggested that the approach proposed by Felsenstein (1981, J. Mol. Evol. 17: 368-376) for topology estimation, as well as its many variations and extensions, differs fundamentally from the maximum likelihood estimation of a conventional statistical parameter. Evidence was presented showing that the Felsenstein approach does not share the asymptotic efficiency of the maximum likelihood estimator of a statistical parameter. Computer simulations were performed to study the probability that MP recovers the true tree under a hierarchy of models of nucleotide substitution; its performance relative to the likelihood method was especially noted. The results appeared to support the intuitive examination of the assumptions underlying MP. When a simple model of nucleotide substitution was assumed to generate data, the probability that MP recovers the true topology could be as high as, or even higher than, that for the likelihood method. When the assumed model became more complex and realistic, e.g., when substitution rates were

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters

    CERN Document Server

    Aguglia, D; Martins, C.D.A.

    2014-01-01

    This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood estimation, is presented, and a sensitivity analysis of the best experimental test to be considered is carried out. The procedure takes into account magnetic saturation and skin effects occurring in the windings during the frequency tests. The presented method is validated by experim...

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

    Science.gov (United States)

    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)

  13. Performance of penalized maximum likelihood in estimation of genetic covariances matrices

    Directory of Open Access Journals (Sweden)

    Meyer Karin

    2011-11-01

    Full Text Available Abstract Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered. Methods An extensive simulation study was carried out to investigate the reduction in average 'loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should

  14. Maximum likelihood estimation of phase-type distributions

    DEFF Research Database (Denmark)

    Esparza, Luz Judith R

    for both univariate and multivariate cases. Methods like the EM algorithm and Markov chain Monte Carlo are applied for this purpose. Furthermore, this thesis provides explicit formulae for computing the Fisher information matrix for discrete and continuous phase-type distributions, which is needed to find......This work is concerned with the statistical inference of phase-type distributions and the analysis of distributions with rational Laplace transform, known as matrix-exponential distributions. The thesis is focused on the estimation of the maximum likelihood parameters of phase-type distributions...... confidence regions for their estimated parameters. Finally, a new general class of distributions, called bilateral matrix-exponential distributions, is defined. These distributions have the entire real line as domain and can be used, for instance, for modelling. In addition, this class of distributions...

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

    Science.gov (United States)

    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

  16. Maximum likelihood of phylogenetic networks.

    Science.gov (United States)

    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

  17. Maximum likelihood estimation of the attenuated ultrasound pulse

    DEFF Research Database (Denmark)

    Rasmussen, Klaus Bolding

    1994-01-01

    The attenuated ultrasound pulse is divided into two parts: a stationary basic pulse and a nonstationary attenuation pulse. A standard ARMA model is used for the basic pulse, and a nonstandard ARMA model is derived for the attenuation pulse. The maximum likelihood estimator of the attenuated...

  18. Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

    Directory of Open Access Journals (Sweden)

    Daigle Bernie J

    2012-05-01

    Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods

  19. Maximum likelihood window for time delay estimation

    International Nuclear Information System (INIS)

    Lee, Young Sup; Yoon, Dong Jin; Kim, Chi Yup

    2004-01-01

    Time delay estimation for the detection of leak location in underground pipelines is critically important. Because the exact leak location depends upon the precision of the time delay between sensor signals due to leak noise and the speed of elastic waves, the research on the estimation of time delay has been one of the key issues in leak lovating with the time arrival difference method. In this study, an optimal Maximum Likelihood window is considered to obtain a better estimation of the time delay. This method has been proved in experiments, which can provide much clearer and more precise peaks in cross-correlation functions of leak signals. The leak location error has been less than 1 % of the distance between sensors, for example the error was not greater than 3 m for 300 m long underground pipelines. Apart from the experiment, an intensive theoretical analysis in terms of signal processing has been described. The improved leak locating with the suggested method is due to the windowing effect in frequency domain, which offers a weighting in significant frequencies.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

  2. Maximum likelihood approach for several stochastic volatility models

    International Nuclear Information System (INIS)

    Camprodon, Jordi; Perelló, Josep

    2012-01-01

    Volatility measures the amplitude of price fluctuations. Despite it being one of the most important quantities in finance, volatility is not directly observable. Here we apply a maximum likelihood method which assumes that price and volatility follow a two-dimensional diffusion process where volatility is the stochastic diffusion coefficient of the log-price dynamics. We apply this method to the simplest versions of the expOU, the OU and the Heston stochastic volatility models and we study their performance in terms of the log-price probability, the volatility probability, and its Mean First-Passage Time. The approach has some predictive power on the future returns amplitude by only knowing the current volatility. The assumed models do not consider long-range volatility autocorrelation and the asymmetric return-volatility cross-correlation but the method still yields very naturally these two important stylized facts. We apply the method to different market indices and with a good performance in all cases. (paper)

  3. Statistical analysis of COMPTEL maximum likelihood-ratio distributions: evidence for a signal from previously undetected AGN

    International Nuclear Information System (INIS)

    Williams, O. R.; Bennett, K.; Much, R.; Schoenfelder, V.; Blom, J. J.; Ryan, J.

    1997-01-01

    The maximum likelihood-ratio method is frequently used in COMPTEL analysis to determine the significance of a point source at a given location. In this paper we do not consider whether the likelihood-ratio at a particular location indicates a detection, but rather whether distributions of likelihood-ratios derived from many locations depart from that expected for source free data. We have constructed distributions of likelihood-ratios by reading values from standard COMPTEL maximum-likelihood ratio maps at positions corresponding to the locations of different categories of AGN. Distributions derived from the locations of Seyfert galaxies are indistinguishable, according to a Kolmogorov-Smirnov test, from those obtained from ''random'' locations, but differ slightly from those obtained from the locations of flat spectrum radio loud quasars, OVVs, and BL Lac objects. This difference is not due to known COMPTEL sources, since regions near these sources are excluded from the analysis. We suggest that it might arise from a number of sources with fluxes below the COMPTEL detection threshold

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

    Science.gov (United States)

    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…

  5. A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation

    Directory of Open Access Journals (Sweden)

    Shu Cai

    2016-12-01

    Full Text Available Direction of arrival (DOA estimation using a uniform linear array (ULA is a classical problem in array signal processing. In this paper, we focus on DOA estimation based on the maximum likelihood (ML criterion, transform the estimation problem into a novel formulation, named as sum-of-squares (SOS, and then solve it using semidefinite programming (SDP. We first derive the SOS and SDP method for DOA estimation in the scenario of a single source and then extend it under the framework of alternating projection for multiple DOA estimation. The simulations demonstrate that the SOS- and SDP-based algorithms can provide stable and accurate DOA estimation when the number of snapshots is small and the signal-to-noise ratio (SNR is low. Moveover, it has a higher spatial resolution compared to existing methods based on the ML criterion.

  6. Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

    Directory of Open Access Journals (Sweden)

    Behrooz Attaran

    2015-01-01

    Full Text Available Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estimation values, which are derived from the vibration signals of test data. The results shows that the performance of the proposed optimized system is better than most previous studies, even though it uses only two features. Effectiveness of the above method is illustrated using obtained bearing vibration data.

  7. Wobbling and LSF-based maximum likelihood expectation maximization reconstruction for wobbling PET

    International Nuclear Information System (INIS)

    Kim, Hang-Keun; Son, Young-Don; Kwon, Dae-Hyuk; Joo, Yohan; Cho, Zang-Hee

    2016-01-01

    Positron emission tomography (PET) is a widely used imaging modality; however, the PET spatial resolution is not yet satisfactory for precise anatomical localization of molecular activities. Detector size is the most important factor because it determines the intrinsic resolution, which is approximately half of the detector size and determines the ultimate PET resolution. Detector size, however, cannot be made too small because both the decreased detection efficiency and the increased septal penetration effect degrade the image quality. A wobbling and line spread function (LSF)-based maximum likelihood expectation maximization (WL-MLEM) algorithm, which combined the MLEM iterative reconstruction algorithm with wobbled sampling and LSF-based deconvolution using the system matrix, was proposed for improving the spatial resolution of PET without reducing the scintillator or detector size. The new algorithm was evaluated using a simulation, and its performance was compared with that of the existing algorithms, such as conventional MLEM and LSF-based MLEM. Simulations demonstrated that the WL-MLEM algorithm yielded higher spatial resolution and image quality than the existing algorithms. The WL-MLEM algorithm with wobbling PET yielded substantially improved resolution compared with conventional algorithms with stationary PET. The algorithm can be easily extended to other iterative reconstruction algorithms, such as maximum a priori (MAP) and ordered subset expectation maximization (OSEM). The WL-MLEM algorithm with wobbling PET may offer improvements in both sensitivity and resolution, the two most sought-after features in PET design. - Highlights: • This paper proposed WL-MLEM algorithm for PET and demonstrated its performance. • WL-MLEM algorithm effectively combined wobbling and line spread function based MLEM. • WL-MLEM provided improvements in the spatial resolution and the PET image quality. • WL-MLEM can be easily extended to the other iterative

  8. Microarray background correction: maximum likelihood estimation for the normal-exponential convolution

    DEFF Research Database (Denmark)

    Silver, Jeremy D; Ritchie, Matthew E; Smyth, Gordon K

    2009-01-01

    exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation...... is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. "MLE" is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data...

  9. Outlier identification procedures for contingency tables using maximum likelihood and $L_1$ estimates

    NARCIS (Netherlands)

    Kuhnt, S.

    2004-01-01

    Observed cell counts in contingency tables are perceived as outliers if they have low probability under an anticipated loglinear Poisson model. New procedures for the identification of such outliers are derived using the classical maximum likelihood estimator and an estimator based on the L1 norm.

  10. Cases in which ancestral maximum likelihood will be confusingly misleading.

    Science.gov (United States)

    Handelman, Tomer; Chor, Benny

    2017-05-07

    Ancestral maximum likelihood (AML) is a phylogenetic tree reconstruction criteria that "lies between" maximum parsimony (MP) and maximum likelihood (ML). ML has long been known to be statistically consistent. On the other hand, Felsenstein (1978) showed that MP is statistically inconsistent, and even positively misleading: There are cases where the parsimony criteria, applied to data generated according to one tree topology, will be optimized on a different tree topology. The question of weather AML is statistically consistent or not has been open for a long time. Mossel et al. (2009) have shown that AML can "shrink" short tree edges, resulting in a star tree with no internal resolution, which yields a better AML score than the original (resolved) model. This result implies that AML is statistically inconsistent, but not that it is positively misleading, because the star tree is compatible with any other topology. We show that AML is confusingly misleading: For some simple, four taxa (resolved) tree, the ancestral likelihood optimization criteria is maximized on an incorrect (resolved) tree topology, as well as on a star tree (both with specific edge lengths), while the tree with the original, correct topology, has strictly lower ancestral likelihood. Interestingly, the two short edges in the incorrect, resolved tree topology are of length zero, and are not adjacent, so this resolved tree is in fact a simple path. While for MP, the underlying phenomenon can be described as long edge attraction, it turns out that here we have long edge repulsion. Copyright © 2017. Published by Elsevier Ltd.

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

    Science.gov (United States)

    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.

  12. Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

    KAUST Repository

    Castrillon, Julio; Genton, Marc G.; Yokota, Rio

    2015-01-01

    We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic

  13. Maximum likelihood reconstruction in fully 3D PET via the SAGE algorithm

    International Nuclear Information System (INIS)

    Ollinger, J.M.; Goggin, A.S.

    1996-01-01

    The SAGE and ordered subsets algorithms have been proposed as fast methods to compute penalized maximum likelihood estimates in PET. We have implemented both for use in fully 3D PET and completed a preliminary evaluation. The technique used to compute the transition matrix is fully described. The evaluation suggests that the ordered subsets algorithm converges much faster than SAGE, but that it stops short of the optimal solution

  14. Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.

    Science.gov (United States)

    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.

  15. A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models.

    Science.gov (United States)

    Chen, Qingxia; Ibrahim, Joseph G

    2014-07-01

    Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

  16. EQPlanar: a maximum-likelihood method for accurate organ activity estimation from whole body planar projections

    International Nuclear Information System (INIS)

    Song, N; Frey, E C; He, B; Wahl, R L

    2011-01-01

    Optimizing targeted radionuclide therapy requires patient-specific estimation of organ doses. The organ doses are estimated from quantitative nuclear medicine imaging studies, many of which involve planar whole body scans. We have previously developed the quantitative planar (QPlanar) processing method and demonstrated its ability to provide more accurate activity estimates than conventional geometric-mean-based planar (CPlanar) processing methods using physical phantom and simulation studies. The QPlanar method uses the maximum likelihood-expectation maximization algorithm, 3D organ volume of interests (VOIs), and rigorous models of physical image degrading factors to estimate organ activities. However, the QPlanar method requires alignment between the 3D organ VOIs and the 2D planar projections and assumes uniform activity distribution in each VOI. This makes application to patients challenging. As a result, in this paper we propose an extended QPlanar (EQPlanar) method that provides independent-organ rigid registration and includes multiple background regions. We have validated this method using both Monte Carlo simulation and patient data. In the simulation study, we evaluated the precision and accuracy of the method in comparison to the original QPlanar method. For the patient studies, we compared organ activity estimates at 24 h after injection with those from conventional geometric mean-based planar quantification using a 24 h post-injection quantitative SPECT reconstruction as the gold standard. We also compared the goodness of fit of the measured and estimated projections obtained from the EQPlanar method to those from the original method at four other time points where gold standard data were not available. In the simulation study, more accurate activity estimates were provided by the EQPlanar method for all the organs at all the time points compared with the QPlanar method. Based on the patient data, we concluded that the EQPlanar method provided a

  17. MAXIMUM-LIKELIHOOD-ESTIMATION OF THE ENTROPY OF AN ATTRACTOR

    NARCIS (Netherlands)

    SCHOUTEN, JC; TAKENS, F; VANDENBLEEK, CM

    In this paper, a maximum-likelihood estimate of the (Kolmogorov) entropy of an attractor is proposed that can be obtained directly from a time series. Also, the relative standard deviation of the entropy estimate is derived; it is dependent on the entropy and on the number of samples used in the

  18. Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    Beer, M.

    1980-01-01

    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 the 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

  1. Narrow band interference cancelation in OFDM: Astructured maximum likelihood approach

    KAUST Repository

    Sohail, Muhammad Sadiq; Al-Naffouri, Tareq Y.; Al-Ghadhban, Samir N.

    2012-01-01

    This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing (ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous

  2. Joint maximum-likelihood magnitudes of presumed underground nuclear test explosions

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  4. Determination of point of maximum likelihood in failure domain using genetic algorithms

    International Nuclear Information System (INIS)

    Obadage, A.S.; Harnpornchai, N.

    2006-01-01

    The point of maximum likelihood in a failure domain yields the highest value of the probability density function in the failure domain. The maximum-likelihood point thus represents the worst combination of random variables that contribute in the failure event. In this work Genetic Algorithms (GAs) with an adaptive penalty scheme have been proposed as a tool for the determination of the maximum likelihood point. The utilization of only numerical values in the GAs operation makes the algorithms applicable to cases of non-linear and implicit single and multiple limit state function(s). The algorithmic simplicity readily extends its application to higher dimensional problems. When combined with Monte Carlo Simulation, the proposed methodology will reduce the computational complexity and at the same time will enhance the possibility in rare-event analysis under limited computational resources. Since, there is no approximation done in the procedure, the solution obtained is considered accurate. Consequently, GAs can be used as a tool for increasing the computational efficiency in the element and system reliability analyses

  5. Multilevel maximum likelihood estimation with application to covariance matrices

    Czech Academy of Sciences Publication Activity Database

    Turčičová, Marie; Mandel, J.; Eben, Kryštof

    Published online: 23 January ( 2018 ) ISSN 0361-0926 R&D Projects: GA ČR GA13-34856S Institutional support: RVO:67985807 Keywords : Fisher information * High dimension * Hierarchical maximum likelihood * Nested parameter spaces * Spectral diagonal covariance model * Sparse inverse covariance model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.311, year: 2016

  6. A simple route to maximum-likelihood estimates of two-locus

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Genetics; Volume 94; Issue 3. A simple route to maximum-likelihood estimates of two-locus recombination fractions under inequality restrictions. Iain L. Macdonald Philasande Nkalashe. Research Note Volume 94 Issue 3 September 2015 pp 479-481 ...

  7. The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

    OpenAIRE

    Rahnamaei, Z.; Nematollahi, N.; Farnoosh, R.

    2012-01-01

    We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

  8. A Maximum Likelihood Approach to Determine Sensor Radiometric Response Coefficients for NPP VIIRS Reflective Solar Bands

    Science.gov (United States)

    Lei, Ning; Chiang, Kwo-Fu; Oudrari, Hassan; Xiong, Xiaoxiong

    2011-01-01

    Optical sensors aboard Earth orbiting satellites such as the next generation Visible/Infrared Imager/Radiometer Suite (VIIRS) assume that the sensors radiometric response in the Reflective Solar Bands (RSB) is described by a quadratic polynomial, in relating the aperture spectral radiance to the sensor Digital Number (DN) readout. For VIIRS Flight Unit 1, the coefficients are to be determined before launch by an attenuation method, although the linear coefficient will be further determined on-orbit through observing the Solar Diffuser. In determining the quadratic polynomial coefficients by the attenuation method, a Maximum Likelihood approach is applied in carrying out the least-squares procedure. Crucial to the Maximum Likelihood least-squares procedure is the computation of the weight. The weight not only has a contribution from the noise of the sensor s digital count, with an important contribution from digitization error, but also is affected heavily by the mathematical expression used to predict the value of the dependent variable, because both the independent and the dependent variables contain random noise. In addition, model errors have a major impact on the uncertainties of the coefficients. The Maximum Likelihood approach demonstrates the inadequacy of the attenuation method model with a quadratic polynomial for the retrieved spectral radiance. We show that using the inadequate model dramatically increases the uncertainties of the coefficients. We compute the coefficient values and their uncertainties, considering both measurement and model errors.

  9. GENERALIZATION OF RAYLEIGH MAXIMUM LIKELIHOOD DESPECKLING FILTER USING QUADRILATERAL KERNELS

    Directory of Open Access Journals (Sweden)

    S. Sridevi

    2013-02-01

    Full Text Available Speckle noise is the most prevalent noise in clinical ultrasound images. It visibly looks like light and dark spots and deduce the pixel intensity as murkiest. Gazing at fetal ultrasound images, the impact of edge and local fine details are more palpable for obstetricians and gynecologists to carry out prenatal diagnosis of congenital heart disease. A robust despeckling filter has to be contrived to proficiently suppress speckle noise and simultaneously preserve the features. The proposed filter is the generalization of Rayleigh maximum likelihood filter by the exploitation of statistical tools as tuning parameters and use different shapes of quadrilateral kernels to estimate the noise free pixel from neighborhood. The performance of various filters namely Median, Kuwahura, Frost, Homogenous mask filter and Rayleigh maximum likelihood filter are compared with the proposed filter in terms PSNR and image profile. Comparatively the proposed filters surpass the conventional filters.

  10. Supervised maximum-likelihood weighting of composite protein networks for complex prediction

    Directory of Open Access Journals (Sweden)

    Yong Chern Han

    2012-12-01

    Full Text Available Abstract Background Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. Results We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Conclusions Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to

  11. The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

    Directory of Open Access Journals (Sweden)

    Z. Rahnamaei

    2012-01-01

    Full Text Available We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

  12. The asymptotic behaviour of the maximum likelihood function of Kriging approximations using the Gaussian correlation function

    CSIR Research Space (South Africa)

    Kok, S

    2012-07-01

    Full Text Available continuously as the correlation function hyper-parameters approach zero. Since the global minimizer of the maximum likelihood function is an asymptote in this case, it is unclear if maximum likelihood estimation (MLE) remains valid. Numerical ill...

  13. Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution

    Directory of Open Access Journals (Sweden)

    Hare Krishna

    2017-01-01

    Full Text Available In this article, we study the geometric distribution under randomly censored data. Maximum likelihood estimators and confidence intervals based on Fisher information matrix are derived for the unknown parameters with randomly censored data. Bayes estimators are also developed using beta priors under generalized entropy and LINEX loss functions. Also, Bayesian credible and highest posterior density (HPD credible intervals are obtained for the parameters. Expected time on test and reliability characteristics are also analyzed in this article. To compare various estimates developed in the article, a Monte Carlo simulation study is carried out. Finally, for illustration purpose, a randomly censored real data set is discussed.

  14. Statistical Bias in Maximum Likelihood Estimators of Item Parameters.

    Science.gov (United States)

    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

  15. Existence and uniqueness of the maximum likelihood estimator for models with a Kronecker product covariance structure

    NARCIS (Netherlands)

    Ros, B.P.; Bijma, F.; de Munck, J.C.; de Gunst, M.C.M.

    2016-01-01

    This paper deals with multivariate Gaussian models for which the covariance matrix is a Kronecker product of two matrices. We consider maximum likelihood estimation of the model parameters, in particular of the covariance matrix. There is no explicit expression for the maximum likelihood estimator

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    Laurence, T.; Chromy, B.

    2010-01-01

    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 of 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 estimator (MLE

  18. MADmap: A Massively Parallel Maximum-Likelihood Cosmic Microwave Background Map-Maker

    Energy Technology Data Exchange (ETDEWEB)

    Cantalupo, Christopher; Borrill, Julian; Jaffe, Andrew; Kisner, Theodore; Stompor, Radoslaw

    2009-06-09

    MADmap is a software application used to produce maximum-likelihood images of the sky from time-ordered data which include correlated noise, such as those gathered by Cosmic Microwave Background (CMB) experiments. It works efficiently on platforms ranging from small workstations to the most massively parallel supercomputers. Map-making is a critical step in the analysis of all CMB data sets, and the maximum-likelihood approach is the most accurate and widely applicable algorithm; however, it is a computationally challenging task. This challenge will only increase with the next generation of ground-based, balloon-borne and satellite CMB polarization experiments. The faintness of the B-mode signal that these experiments seek to measure requires them to gather enormous data sets. MADmap is already being run on up to O(1011) time samples, O(108) pixels and O(104) cores, with ongoing work to scale to the next generation of data sets and supercomputers. We describe MADmap's algorithm based around a preconditioned conjugate gradient solver, fast Fourier transforms and sparse matrix operations. We highlight MADmap's ability to address problems typically encountered in the analysis of realistic CMB data sets and describe its application to simulations of the Planck and EBEX experiments. The massively parallel and distributed implementation is detailed and scaling complexities are given for the resources required. MADmap is capable of analysing the largest data sets now being collected on computing resources currently available, and we argue that, given Moore's Law, MADmap will be capable of reducing the most massive projected data sets.

  19. An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search

    NARCIS (Netherlands)

    Bergboer, N.H.; Verdult, V.; Verhaegen, M.H.G.

    2002-01-01

    We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting

  20. Maximum likelihood estimation of biophysical parameters of synaptic receptors from macroscopic currents

    Directory of Open Access Journals (Sweden)

    Andrey eStepanyuk

    2014-10-01

    Full Text Available Dendritic integration and neuronal firing patterns strongly depend on biophysical properties of synaptic ligand-gated channels. However, precise estimation of biophysical parameters of these channels in their intrinsic environment is complicated and still unresolved problem. Here we describe a novel method based on a maximum likelihood approach that allows to estimate not only the unitary current of synaptic receptor channels but also their multiple conductance levels, kinetic constants, the number of receptors bound with a neurotransmitter and the peak open probability from experimentally feasible number of postsynaptic currents. The new method also improves the accuracy of evaluation of unitary current as compared to the peak-scaled non-stationary fluctuation analysis, leading to a possibility to precisely estimate this important parameter from a few postsynaptic currents recorded in steady-state conditions. Estimation of unitary current with this method is robust even if postsynaptic currents are generated by receptors having different kinetic parameters, the case when peak-scaled non-stationary fluctuation analysis is not applicable. Thus, with the new method, routinely recorded postsynaptic currents could be used to study the properties of synaptic receptors in their native biochemical environment.

  1. Modified Moment, Maximum Likelihood and Percentile Estimators for the Parameters of the Power Function Distribution

    Directory of Open Access Journals (Sweden)

    Azam Zaka

    2014-10-01

    Full Text Available This paper is concerned with the modifications of maximum likelihood, moments and percentile estimators of the two parameter Power function distribution. Sampling behavior of the estimators is indicated by Monte Carlo simulation. For some combinations of parameter values, some of the modified estimators appear better than the traditional maximum likelihood, moments and percentile estimators with respect to bias, mean square error and total deviation.

  2. Likelihood devices in spatial statistics

    NARCIS (Netherlands)

    Zwet, E.W. van

    1999-01-01

    One of the main themes of this thesis is the application to spatial data of modern semi- and nonparametric methods. Another, closely related theme is maximum likelihood estimation from spatial data. Maximum likelihood estimation is not common practice in spatial statistics. The method of moments

  3. Preliminary attempt on maximum likelihood tomosynthesis reconstruction of DEI data

    International Nuclear Information System (INIS)

    Wang Zhentian; Huang Zhifeng; Zhang Li; Kang Kejun; Chen Zhiqiang; Zhu Peiping

    2009-01-01

    Tomosynthesis is a three-dimension reconstruction method that can remove the effect of superimposition with limited angle projections. It is especially promising in mammography where radiation dose is concerned. In this paper, we propose a maximum likelihood tomosynthesis reconstruction algorithm (ML-TS) on the apparent absorption data of diffraction enhanced imaging (DEI). The motivation of this contribution is to develop a tomosynthesis algorithm in low-dose or noisy circumstances and make DEI get closer to clinic application. The theoretical statistical models of DEI data in physics are analyzed and the proposed algorithm is validated with the experimental data at the Beijing Synchrotron Radiation Facility (BSRF). The results of ML-TS have better contrast compared with the well known 'shift-and-add' algorithm and FBP algorithm. (authors)

  4. Marginal Maximum Likelihood Estimation of Item Response Models in R

    Directory of Open Access Journals (Sweden)

    Matthew S. Johnson

    2007-02-01

    Full Text Available Item response theory (IRT models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.

  5. Maximum likelihood estimation of the position of a radiating source in a waveguide

    International Nuclear Information System (INIS)

    Hinich, M.J.

    1979-01-01

    An array of sensors is receiving radiation from a source of interest. The source and the array are in a one- or two-dimensional waveguide. The maximum-likelihood estimators of the coordinates of the source are analyzed under the assumptions that the noise field is Gaussian. The Cramer-Rao lower bound is of the order of the number of modes which define the source excitation function. The results show that the accuracy of the maximum likelihood estimator of source depth using a vertical array in a infinite horizontal waveguide (such as the ocean) is limited by the number of modes detected by the array regardless of the array size

  6. Efficient algorithms for maximum likelihood decoding in the surface code

    Science.gov (United States)

    Bravyi, Sergey; Suchara, Martin; Vargo, Alexander

    2014-09-01

    We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the two-dimensional surface code with a noiseless syndrome extraction. First, we show how to implement MLD exactly in time O (n2), where n is the number of code qubits. Our implementation uses a reduction from MLD to simulation of matchgate quantum circuits. This reduction however requires a special noise model with independent bit-flip and phase-flip errors. Secondly, we show how to implement MLD approximately for more general noise models using matrix product states (MPS). Our implementation has running time O (nχ3), where χ is a parameter that controls the approximation precision. The key step of our algorithm, borrowed from the density matrix renormalization-group method, is a subroutine for contracting a tensor network on the two-dimensional grid. The subroutine uses MPS with a bond dimension χ to approximate the sequence of tensors arising in the course of contraction. We benchmark the MPS-based decoder against the standard minimum weight matching decoder observing a significant reduction of the logical error probability for χ ≥4.

  7. Fast Maximum-Likelihood Decoder for Quasi-Orthogonal Space-Time Block Code

    Directory of Open Access Journals (Sweden)

    Adel Ahmadi

    2015-01-01

    Full Text Available Motivated by the decompositions of sphere and QR-based methods, in this paper we present an extremely fast maximum-likelihood (ML detection approach for quasi-orthogonal space-time block code (QOSTBC. The proposed algorithm with a relatively simple design exploits structure of quadrature amplitude modulation (QAM constellations to achieve its goal and can be extended to any arbitrary constellation. Our decoder utilizes a new decomposition technique for ML metric which divides the metric into independent positive parts and a positive interference part. Search spaces of symbols are substantially reduced by employing the independent parts and statistics of noise. Symbols within the search spaces are successively evaluated until the metric is minimized. Simulation results confirm that the proposed decoder’s performance is superior to many of the recently published state-of-the-art solutions in terms of complexity level. More specifically, it was possible to verify that application of the new algorithms with 1024-QAM would decrease the computational complexity compared to state-of-the-art solution with 16-QAM.

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

    International Nuclear Information System (INIS)

    Brendel, Bernhard; Teuffenbach, Maximilian von; Noël, Peter B.; Pfeiffer, Franz; Koehler, Thomas

    2016-01-01

    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 penalty 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

  9. Comparison of least-squares vs. maximum likelihood estimation for standard spectrum technique of β−γ coincidence spectrum analysis

    International Nuclear Information System (INIS)

    Lowrey, Justin D.; Biegalski, Steven R.F.

    2012-01-01

    The spectrum deconvolution analysis tool (SDAT) software code was written and tested at The University of Texas at Austin utilizing the standard spectrum technique to determine activity levels of Xe-131m, Xe-133m, Xe-133, and Xe-135 in β–γ coincidence spectra. SDAT was originally written to utilize the method of least-squares to calculate the activity of each radionuclide component in the spectrum. Recently, maximum likelihood estimation was also incorporated into the SDAT tool. This is a robust statistical technique to determine the parameters that maximize the Poisson distribution likelihood function of the sample data. In this case it is used to parameterize the activity level of each of the radioxenon components in the spectra. A new test dataset was constructed utilizing Xe-131m placed on a Xe-133 background to compare the robustness of the least-squares and maximum likelihood estimation methods for low counting statistics data. The Xe-131m spectra were collected independently from the Xe-133 spectra and added to generate the spectra in the test dataset. The true independent counts of Xe-131m and Xe-133 are known, as they were calculated before the spectra were added together. Spectra with both high and low counting statistics are analyzed. Studies are also performed by analyzing only the 30 keV X-ray region of the β–γ coincidence spectra. Results show that maximum likelihood estimation slightly outperforms least-squares for low counting statistics data.

  10. Maximum Likelihood Joint Tracking and Association in Strong Clutter

    Directory of Open Access Journals (Sweden)

    Leonid I. Perlovsky

    2013-01-01

    Full Text Available We have developed a maximum likelihood formulation for a joint detection, tracking and association problem. An efficient non-combinatorial algorithm for this problem is developed in case of strong clutter for radar data. By using an iterative procedure of the dynamic logic process “from vague-to-crisp” explained in the paper, the new tracker overcomes the combinatorial complexity of tracking in highly-cluttered scenarios and results in an orders-of-magnitude improvement in signal-to-clutter ratio.

  11. A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection.

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-09-01

    In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

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

    Science.gov (United States)

    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…

  13. Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation

    OpenAIRE

    Rajiv D. Banker

    1993-01-01

    This paper provides a formal statistical basis for the efficiency evaluation techniques of data envelopment analysis (DEA). DEA estimators of the best practice monotone increasing and concave production function are shown to be also maximum likelihood estimators if the deviation of actual output from the efficient output is regarded as a stochastic variable with a monotone decreasing probability density function. While the best practice frontier estimator is biased below the theoretical front...

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

    Science.gov (United States)

    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.

  15. Maximum likelihood positioning algorithm for high-resolution PET scanners

    International Nuclear Information System (INIS)

    Gross-Weege, Nicolas; Schug, David; Hallen, Patrick; Schulz, Volkmar

    2016-01-01

    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: 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 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 of the ML

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

    DEFF Research Database (Denmark)

    Scheike, Thomas; Juul, Anders

    2004-01-01

    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 hazard...

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

    Science.gov (United States)

    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…

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

    Energy Technology Data Exchange (ETDEWEB)

    Gopich, Irina V. [Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892 (United States)

    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 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.

  19. Truncation artifact suppression in cone-beam radionuclide transmission CT using maximum likelihood techniques: evaluation with human subjects

    International Nuclear Information System (INIS)

    Manglos, S.H.

    1992-01-01

    Transverse image truncation can be a serious problem for human imaging using cone-beam transmission CT (CB-CT) implemented on a conventional rotating gamma camera. This paper presents a reconstruction method to reduce or eliminate the artifacts resulting from the truncation. The method uses a previously published transmission maximum likelihood EM algorithm, adapted to the cone-beam geometry. The reconstruction method is evaluated qualitatively using three human subjects of various dimensions and various degrees of truncation. (author)

  20. A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.

    2013-01-01

    We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a

  1. Maximum likelihood sequence estimation for optical complex direct modulation.

    Science.gov (United States)

    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.

  2. Maximum likelihood unit rooting test in the presence GARCH: A new test with increased power

    OpenAIRE

    Cook , Steve

    2008-01-01

    Abstract The literature on testing the unit root hypothesis in the presence of GARCH errors is extended. A new test based upon the combination of local-to-unity detrending and joint maximum likelihood estimation of the autoregressive parameter and GARCH process is presented. The finite sample distribution of the test is derived under alternative decisions regarding the deterministic terms employed. Using Monte Carlo simulation, the newly proposed ML t-test is shown to exhibit incre...

  3. Analysis of QCD sum rule based on the maximum entropy method

    International Nuclear Information System (INIS)

    Gubler, Philipp

    2012-01-01

    QCD sum rule was developed about thirty years ago and has been used up to the present to calculate various physical quantities like hadrons. It has been, however, needed to assume 'pole + continuum' for the spectral function in the conventional analyses. Application of this method therefore came across with difficulties when the above assumption is not satisfied. In order to avoid this difficulty, analysis to make use of the maximum entropy method (MEM) has been developed by the present author. It is reported here how far this new method can be successfully applied. In the first section, the general feature of the QCD sum rule is introduced. In section 2, it is discussed why the analysis by the QCD sum rule based on the MEM is so effective. In section 3, the MEM analysis process is described, and in the subsection 3.1 likelihood function and prior probability are considered then in subsection 3.2 numerical analyses are picked up. In section 4, some cases of applications are described starting with ρ mesons, then charmoniums in the finite temperature and finally recent developments. Some figures of the spectral functions are shown. In section 5, summing up of the present analysis method and future view are given. (S. Funahashi)

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

    Directory of Open Access Journals (Sweden)

    Manuel Gil

    2014-09-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  6. Gravitational wave chirp search: no-signal cumulative distribution of the maximum likelihood detection statistic

    International Nuclear Information System (INIS)

    Croce, R P; Demma, Th; Longo, M; Marano, S; Matta, V; Pierro, V; Pinto, I M

    2003-01-01

    The cumulative distribution of the supremum of a set (bank) of correlators is investigated in the context of maximum likelihood detection of gravitational wave chirps from coalescing binaries with unknown parameters. Accurate (lower-bound) approximants are introduced based on a suitable generalization of previous results by Mohanty. Asymptotic properties (in the limit where the number of correlators goes to infinity) are highlighted. The validity of numerical simulations made on small-size banks is extended to banks of any size, via a Gaussian correlation inequality

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

    Science.gov (United States)

    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.

  8. On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution

    Directory of Open Access Journals (Sweden)

    Navid Feroze

    2015-12-01

    Full Text Available Burr type III is an important distribution used to model the failure time data. The paper addresses the problem of estimation of parameters of the Burr type III distribution based on maximum likelihood estimation (MLE when the samples are left censored. As the closed form expression for the MLEs of the parameters cannot be derived, the approximate solutions have been obtained through iterative procedures. An extensive simulation study has been carried out to investigate the performance of the estimators with respect to sample size, censoring rate and true parametric values. A real life example has also been presented. The study revealed that the proposed estimators are consistent and capable of providing efficient results under small to moderate samples.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. PTree: pattern-based, stochastic search for maximum parsimony phylogenies

    Directory of Open Access Journals (Sweden)

    Ivan Gregor

    2013-06-01

    Full Text Available Phylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. Here, we describe a stochastic search method for a maximum parsimony tree, implemented in a software package we named PTree. Our method is based on a new pattern-based technique that enables us to infer intermediate sequences efficiently where the incorporation of these sequences in the current tree topology yields a phylogenetic tree with a lower cost. Evaluation across multiple datasets showed that our method is comparable to the algorithms implemented in PAUP* or TNT, which are widely used by the bioinformatics community, in terms of topological accuracy and runtime. We show that our method can process large-scale datasets of 1,000–8,000 sequences. We believe that our novel pattern-based method enriches the current set of tools and methods for phylogenetic tree inference. The software is available under: http://algbio.cs.uni-duesseldorf.de/webapps/wa-download/.

  12. PTree: pattern-based, stochastic search for maximum parsimony phylogenies.

    Science.gov (United States)

    Gregor, Ivan; Steinbrück, Lars; McHardy, Alice C

    2013-01-01

    Phylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. Here, we describe a stochastic search method for a maximum parsimony tree, implemented in a software package we named PTree. Our method is based on a new pattern-based technique that enables us to infer intermediate sequences efficiently where the incorporation of these sequences in the current tree topology yields a phylogenetic tree with a lower cost. Evaluation across multiple datasets showed that our method is comparable to the algorithms implemented in PAUP* or TNT, which are widely used by the bioinformatics community, in terms of topological accuracy and runtime. We show that our method can process large-scale datasets of 1,000-8,000 sequences. We believe that our novel pattern-based method enriches the current set of tools and methods for phylogenetic tree inference. The software is available under: http://algbio.cs.uni-duesseldorf.de/webapps/wa-download/.

  13. Safe semi-supervised learning based on weighted likelihood.

    Science.gov (United States)

    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(')methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  15. Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.

    Directory of Open Access Journals (Sweden)

    Zhang Zhang

    2009-06-01

    Full Text Available A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. The clustering model may be evaluated via model selection using the Akaike Information Criterion, the corrected Akaike Information Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.

  16. Likelihood-Based Inference of B Cell Clonal Families.

    Directory of Open Access Journals (Sweden)

    Duncan K Ralph

    2016-10-01

    Full Text Available The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.

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

    Science.gov (United States)

    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…

  18. Maximum likelihood pedigree reconstruction using integer linear programming.

    Science.gov (United States)

    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.

  19. An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

    Science.gov (United States)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

  20. Generalized empirical likelihood methods for analyzing longitudinal data

    KAUST Repository

    Wang, S.

    2010-02-16

    Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.

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

    Science.gov (United States)

    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.

  2. Simultaneous determination of exponential background and Gaussian peak functions in gamma ray scintillation spectrometers by maximum likelihood technique

    International Nuclear Information System (INIS)

    Eisler, P.; Youl, S.; Lwin, T.; Nelson, G.

    1983-01-01

    Simultaneous fitting of peaks and background functions from gamma-ray spectrometry using multichannel pulse height analysis is considered. The specific case of Gaussian peak and exponential background is treated in detail with respect to simultaneous estimation of both functions by using a technique which incorporates maximum likelihood method as well as a graphical method. Theoretical expressions for the standard errors of the estimates are also obtained. The technique is demonstrated for two experimental data sets. (orig.)

  3. Maximum Likelihood DOA Estimation of Multiple Wideband Sources in the Presence of Nonuniform Sensor Noise

    Directory of Open Access Journals (Sweden)

    K. Yao

    2007-12-01

    Full Text Available We investigate the maximum likelihood (ML direction-of-arrival (DOA estimation of multiple wideband sources in the presence of unknown nonuniform sensor noise. New closed-form expression for the direction estimation Cramér-Rao-Bound (CRB has been derived. The performance of the conventional wideband uniform ML estimator under nonuniform noise has been studied. In order to mitigate the performance degradation caused by the nonuniformity of the noise, a new deterministic wideband nonuniform ML DOA estimator is derived and two associated processing algorithms are proposed. The first algorithm is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the DOAs and the noise nuisance parameters, while the second is a noniterative algorithm that maximizes the derived approximately concentrated log-likelihood function. The performance of the proposed algorithms is tested through extensive computer simulations. Simulation results show the stepwise-concentrated ML algorithm (SC-ML requires only a few iterations to converge and both the SC-ML and the approximately-concentrated ML algorithm (AC-ML attain a solution close to the derived CRB at high signal-to-noise ratio.

  4. Cox regression with missing covariate data using a modified partial likelihood method

    DEFF Research Database (Denmark)

    Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.

    2016-01-01

    Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard...

  5. Penalized maximum-likelihood sinogram restoration for dual focal spot computed tomography

    International Nuclear Information System (INIS)

    Forthmann, P; Koehler, T; Begemann, P G C; Defrise, M

    2007-01-01

    Due to various system non-idealities, the raw data generated by a computed tomography (CT) machine are not readily usable for reconstruction. Although the deterministic nature of corruption effects such as crosstalk and afterglow permits correction by deconvolution, there is a drawback because deconvolution usually amplifies noise. Methods that perform raw data correction combined with noise suppression are commonly termed sinogram restoration methods. The need for sinogram restoration arises, for example, when photon counts are low and non-statistical reconstruction algorithms such as filtered backprojection are used. Many modern CT machines offer a dual focal spot (DFS) mode, which serves the goal of increased radial sampling by alternating the focal spot between two positions on the anode plate during the scan. Although the focal spot mode does not play a role with respect to how the data are affected by the above-mentioned corruption effects, it needs to be taken into account if regularized sinogram restoration is to be applied to the data. This work points out the subtle difference in processing that sinogram restoration for DFS requires, how it is correctly employed within the penalized maximum-likelihood sinogram restoration algorithm and what impact it has on image quality

  6. Maximum-Likelihood Sequence Detection of Multiple Antenna Systems over Dispersive Channels via Sphere Decoding

    Directory of Open Access Journals (Sweden)

    Hassibi Babak

    2002-01-01

    Full Text Available Multiple antenna systems are capable of providing high data rate transmissions over wireless channels. When the channels are dispersive, the signal at each receive antenna is a combination of both the current and past symbols sent from all transmit antennas corrupted by noise. The optimal receiver is a maximum-likelihood sequence detector and is often considered to be practically infeasible due to high computational complexity (exponential in number of antennas and channel memory. Therefore, in practice, one often settles for a less complex suboptimal receiver structure, typically with an equalizer meant to suppress both the intersymbol and interuser interference, followed by the decoder. We propose a sphere decoding for the sequence detection in multiple antenna communication systems over dispersive channels. The sphere decoding provides the maximum-likelihood estimate with computational complexity comparable to the standard space-time decision-feedback equalizing (DFE algorithms. The performance and complexity of the sphere decoding are compared with the DFE algorithm by means of simulations.

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

    Science.gov (United States)

    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.

  8. Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators.

    Science.gov (United States)

    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.

  9. A Fast Algorithm for Maximum Likelihood Estimation of Harmonic Chirp Parameters

    DEFF Research Database (Denmark)

    Jensen, Tobias Lindstrøm; Nielsen, Jesper Kjær; Jensen, Jesper Rindom

    2017-01-01

    . A statistically efficient estimator for extracting the parameters of the harmonic chirp model in additive white Gaussian noise is the maximum likelihood (ML) estimator which recently has been demonstrated to be robust to noise and accurate --- even when the model order is unknown. The main drawback of the ML......The analysis of (approximately) periodic signals is an important element in numerous applications. One generalization of standard periodic signals often occurring in practice are harmonic chirp signals where the instantaneous frequency increases/decreases linearly as a function of time...

  10. Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters.

    Directory of Open Access Journals (Sweden)

    Kaarina Matilainen

    Full Text Available Estimation of variance components by Monte Carlo (MC expectation maximization (EM restricted maximum likelihood (REML is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR, where the information matrix was generated via sampling; MC average information(AI, where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

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

    Science.gov (United States)

    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...

  12. Conditional maximum-entropy method for selecting prior distributions in Bayesian statistics

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  14. Penalised Maximum Likelihood Simultaneous Longitudinal PET Image Reconstruction with Difference-Image Priors.

    Science.gov (United States)

    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 quantify changes in functional behaviour in tumours 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 penalising 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 2D simulation studies and a 3D real dataset case study. We have previously proposed a simultaneous difference-image-based penalised 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 i) low entropy (DE-PML), and ii) 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 tumour datasets and compared to standard maximum likelihood expectation-maximisation (MLEM) reconstructions. These 2D simulation studies explored the effects of penalty strengths, tumour behaviour, and inter-scan 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 those seen when using standard

  15. BER and optimal power allocation for amplify-and-forward relaying using pilot-aided maximum likelihood estimation

    KAUST Repository

    Wang, Kezhi

    2014-10-01

    Bit error rate (BER) and outage probability for amplify-and-forward (AF) relaying systems with two different channel estimation methods, disintegrated channel estimation and cascaded channel estimation, using pilot-aided maximum likelihood method in slowly fading Rayleigh channels are derived. Based on the BERs, the optimal values of pilot power under the total transmitting power constraints at the source and the optimal values of pilot power under the total transmitting power constraints at the relay are obtained, separately. Moreover, the optimal power allocation between the pilot power at the source, the pilot power at the relay, the data power at the source and the data power at the relay are obtained when their total transmitting power is fixed. Numerical results show that the derived BER expressions match with the simulation results. They also show that the proposed systems with optimal power allocation outperform the conventional systems without power allocation under the same other conditions. In some cases, the gain could be as large as several dB\\'s in effective signal-to-noise ratio.

  16. BER and optimal power allocation for amplify-and-forward relaying using pilot-aided maximum likelihood estimation

    KAUST Repository

    Wang, Kezhi; Chen, Yunfei; Alouini, Mohamed-Slim; Xu, Feng

    2014-01-01

    Bit error rate (BER) and outage probability for amplify-and-forward (AF) relaying systems with two different channel estimation methods, disintegrated channel estimation and cascaded channel estimation, using pilot-aided maximum likelihood method in slowly fading Rayleigh channels are derived. Based on the BERs, the optimal values of pilot power under the total transmitting power constraints at the source and the optimal values of pilot power under the total transmitting power constraints at the relay are obtained, separately. Moreover, the optimal power allocation between the pilot power at the source, the pilot power at the relay, the data power at the source and the data power at the relay are obtained when their total transmitting power is fixed. Numerical results show that the derived BER expressions match with the simulation results. They also show that the proposed systems with optimal power allocation outperform the conventional systems without power allocation under the same other conditions. In some cases, the gain could be as large as several dB's in effective signal-to-noise ratio.

  17. Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model

    International Nuclear Information System (INIS)

    Edwards, Darrin C.; Kupinski, Matthew A.; Metz, Charles E.; Nishikawa, Robert M.

    2002-01-01

    We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well

  18. PTree: pattern-based, stochastic search for maximum parsimony phylogenies

    OpenAIRE

    Gregor, Ivan; Steinbr?ck, Lars; McHardy, Alice C.

    2013-01-01

    Phylogenetic reconstruction is vital to analyzing the evolutionary relationship of genes within and across populations of different species. Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. Here, we ...

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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…

  1. Generalized empirical likelihood methods for analyzing longitudinal data

    KAUST Repository

    Wang, S.; Qian, L.; Carroll, R. J.

    2010-01-01

    Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  4. Maximum likelihood estimation for cytogenetic dose-response curves

    International Nuclear Information System (INIS)

    Frome, E.L.; DuFrain, R.J.

    1986-01-01

    In vitro dose-response curves are used to describe the relation between chromosome aberrations and radiation dose for human lymphocytes. The lymphocytes are exposed to low-LET radiation, and the resulting dicentric chromosome aberrations follow the Poisson distribution. The expected yield depends on both the magnitude and the temporal distribution of the dose. A general dose-response model that describes this relation has been presented by Kellerer and Rossi (1972, Current Topics on Radiation Research Quarterly 8, 85-158; 1978, Radiation Research 75, 471-488) using the theory of dual radiation action. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting dose-time-response models are intrinsically nonlinear in the parameters. A general-purpose maximum likelihood estimation procedure is described, and estimation for the nonlinear models is illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure

  5. Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

    Directory of Open Access Journals (Sweden)

    César da Silva Chagas

    2013-04-01

    Full Text Available Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI, derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs was greater than of the classic Maximum Likelihood Classifier (MLC. Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 % was superior to the MLC map (57.94 %. The main errors when using the two classifiers were caused by: a the geological heterogeneity of the area coupled with problems related to the geological map; b the depth of lithic contact and/or rock exposure, and c problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

  6. Use of Maximum Likelihood-Mixed Models to select stable reference genes: a case of heat stress response in sheep

    Directory of Open Access Journals (Sweden)

    Salces Judit

    2011-08-01

    Full Text Available Abstract Background Reference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments. Several procedures and companion software have been proposed to find the most stable genes. Model based procedures are attractive because they provide a solid statistical framework. NormFinder, a widely used software, uses a model based method. The pairwise comparison procedure implemented in GeNorm is a simpler procedure but one of the most extensively used. In the present work a statistical approach based in Maximum Likelihood estimation under mixed models was tested and compared with NormFinder and geNorm softwares. Sixteen candidate genes were tested in whole blood samples from control and heat stressed sheep. Results A model including gene and treatment as fixed effects, sample (animal, gene by treatment, gene by sample and treatment by sample interactions as random effects with heteroskedastic residual variance in gene by treatment levels was selected using goodness of fit and predictive ability criteria among a variety of models. Mean Square Error obtained under the selected model was used as indicator of gene expression stability. Genes top and bottom ranked by the three approaches were similar; however, notable differences for the best pair of genes selected for each method and the remaining genes of the rankings were shown. Differences among the expression values of normalized targets for each statistical approach were also found. Conclusions Optimal statistical properties of Maximum Likelihood estimation joined to mixed model flexibility allow for more accurate estimation of expression stability of genes under many different situations. Accurate selection of reference genes has a direct impact over the normalized expression values of a given target gene. This may be critical when the aim of the study is to compare expression rate differences among samples under different environmental

  7. Likelihood Inference of Nonlinear Models Based on a Class of Flexible Skewed Distributions

    Directory of Open Access Journals (Sweden)

    Xuedong Chen

    2014-01-01

    Full Text Available This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN distribution, which is proposed within a general framework of flexible skew-symmetric (FSS distributions by combining with skew-t-normal (STN distribution. In comparison with the common skewed distributions such as skew normal (SN, and skew-t (ST as well as scale mixtures of skew normal (SMSN, the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach.

  8. Measurement of the Top Quark Mass by Dynamical Likelihood Method using the Lepton + Jets Events with the Collider Detector at Fermilab

    Energy Technology Data Exchange (ETDEWEB)

    Kubo, Taichi [Univ. of Tsukuba (Japan)

    2008-02-01

    We have measured the top quark mass with the dynamical likelihood method. The data corresponding to an integrated luminosity of 1.7fb-1 was collected in proton antiproton collisions at a center of mass energy of 1.96 TeV with the CDF detector at Fermilab Tevatron during the period March 2002-March 2007. We select t$\\bar{t}$ pair production candidates by requiring one high energy lepton and four jets, in which at least one of jets must be tagged as a b-jet. In order to reconstruct the top quark mass, we use the dynamical likelihood method based on maximum likelihood method where a likelihood is defined as the differential cross section multiplied by the transfer function from observed quantities to parton quantities, as a function of the top quark mass and the jet energy scale(JES). With this method, we measure the top quark mass to be 171.6 ± 2.0 (stat.+ JES) ± 1.3(syst.) = 171.6 ± 2.4 GeV/c2.

  9. 2-Step Maximum Likelihood Channel Estimation for Multicode DS-CDMA with Frequency-Domain Equalization

    Science.gov (United States)

    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.

  10. A maximum-likelihood reconstruction algorithm for tomographic gamma-ray nondestructive assay

    International Nuclear Information System (INIS)

    Prettyman, T.H.; Estep, R.J.; Cole, R.A.; Sheppard, G.A.

    1994-01-01

    A new tomographic reconstruction algorithm for nondestructive assay with high resolution gamma-ray spectroscopy (HRGS) is presented. The reconstruction problem is formulated using a maximum-likelihood approach in which the statistical structure of both the gross and continuum measurements used to determine the full-energy response in HRGS is precisely modeled. An accelerated expectation-maximization algorithm is used to determine the optimal solution. The algorithm is applied to safeguards and environmental assays of large samples (for example, 55-gal. drums) in which high continuum levels caused by Compton scattering are routinely encountered. Details of the implementation of the algorithm and a comparative study of the algorithm's performance are presented

  11. Maximum likelihood reconstruction for pinhole SPECT with a displaced center-of-rotation

    International Nuclear Information System (INIS)

    Li, J.; Jaszczak, R.J.; Coleman, R.E.

    1995-01-01

    In this paper, the authors describe the implementation of a maximum likelihood (ML), algorithm using expectation maximization (EM) for pin-hole SPECT with a displaced center-of-rotation. A ray-tracing technique is used in implementing the ML-EM algorithm. The proposed ML-EM algorithm is able to correct the center of rotation displacement which can be characterized by two orthogonal components. The algorithm is tested using experimentally acquired data, and the results demonstrate that the pinhole ML-EM algorithm is able to correct artifacts associated with the center-of-rotation displacement

  12. Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

    KAUST Repository

    Castrillon, Julio

    2015-11-10

    We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic parameters of the model are filtered out thus enabling the estimation of the covariance parameters to be decoupled from the deterministic component. Moreover, the multi-level covariance matrix of the contrasts exhibit fast decay that is dependent on the smoothness of the covariance function. Due to the fast decay of the multi-level covariance matrix coefficients only a small set is computed with a level dependent criterion. We demonstrate our approach on problems of up to 512,000 observations with a Matérn covariance function and highly irregular placements of the observations. In addition, these problems are numerically unstable and hard to solve with traditional methods.

  13. A simulation study of likelihood inference procedures in rayleigh distribution with censored data

    International Nuclear Information System (INIS)

    Baklizi, S. A.; Baker, H. M.

    2001-01-01

    Inference procedures based on the likelihood function are considered for the one parameter Rayleigh distribution with type1 and type 2 censored data. Using simulation techniques, the finite sample performances of the maximum likelihood estimator and the large sample likelihood interval estimation procedures based on the Wald, the Rao, and the likelihood ratio statistics are investigated. It appears that the maximum likelihood estimator is unbiased. The approximate variance estimates obtained from the asymptotic normal distribution of the maximum likelihood estimator are accurate under type 2 censored data while they tend to be smaller than the actual variances when considering type1 censored data of small size. It appears also that interval estimation based on the Wald and Rao statistics need much more sample size than interval estimation based on the likelihood ratio statistic to attain reasonable accuracy. (authors). 15 refs., 4 tabs

  14. Statistical analysis of maximum likelihood estimator images of human brain FDG PET studies

    International Nuclear Information System (INIS)

    Llacer, J.; Veklerov, E.; Hoffman, E.J.; Nunez, J.; Coakley, K.J.

    1993-01-01

    The work presented in this paper evaluates the statistical characteristics of regional bias and expected error in reconstructions of real PET data of human brain fluorodeoxiglucose (FDG) studies carried out by the maximum likelihood estimator (MLE) method with a robust stopping rule, and compares them with the results of filtered backprojection (FBP) reconstructions and with the method of sieves. The task that the authors have investigated is that of quantifying radioisotope uptake in regions-of-interest (ROI's). They first describe a robust methodology for the use of the MLE method with clinical data which contains only one adjustable parameter: the kernel size for a Gaussian filtering operation that determines final resolution and expected regional error. Simulation results are used to establish the fundamental characteristics of the reconstructions obtained by out methodology, corresponding to the case in which the transition matrix is perfectly known. Then, data from 72 independent human brain FDG scans from four patients are used to show that the results obtained from real data are consistent with the simulation, although the quality of the data and of the transition matrix have an effect on the final outcome

  15. Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy.

    Science.gov (United States)

    McGuire, Jimmy A; Witt, Christopher C; Altshuler, Douglas L; Remsen, J V

    2007-10-01

    Hummingbirds are an important model system in avian biology, but to date the group has been the subject of remarkably few phylogenetic investigations. Here we present partitioned Bayesian and maximum likelihood phylogenetic analyses for 151 of approximately 330 species of hummingbirds and 12 outgroup taxa based on two protein-coding mitochondrial genes (ND2 and ND4), flanking tRNAs, and two nuclear introns (AK1 and BFib). We analyzed these data under several partitioning strategies ranging between unpartitioned and a maximum of nine partitions. In order to select a statistically justified partitioning strategy following partitioned Bayesian analysis, we considered four alternative criteria including Bayes factors, modified versions of the Akaike information criterion for small sample sizes (AIC(c)), Bayesian information criterion (BIC), and a decision-theoretic methodology (DT). Following partitioned maximum likelihood analyses, we selected a best-fitting strategy using hierarchical likelihood ratio tests (hLRTS), the conventional AICc, BIC, and DT, concluding that the most stringent criterion, the performance-based DT, was the most appropriate methodology for selecting amongst partitioning strategies. In the context of our well-resolved and well-supported phylogenetic estimate, we consider the historical biogeography of hummingbirds using ancestral state reconstructions of (1) primary geographic region of occurrence (i.e., South America, Central America, North America, Greater Antilles, Lesser Antilles), (2) Andean or non-Andean geographic distribution, and (3) minimum elevational occurrence. These analyses indicate that the basal hummingbird assemblages originated in the lowlands of South America, that most of the principle clades of hummingbirds (all but Mountain Gems and possibly Bees) originated on this continent, and that there have been many (at least 30) independent invasions of other primary landmasses, especially Central America.

  16. Maximum likelihood estimation for cytogenetic dose-response curves

    International Nuclear Information System (INIS)

    Frome, E.L; DuFrain, R.J.

    1983-10-01

    In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa[γd + g(t, tau)d 2 ], where t is the time and d is dose. The coefficient of the d 2 term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure

  17. Maximum likelihood estimation for cytogenetic dose-response curves

    Energy Technology Data Exchange (ETDEWEB)

    Frome, E.L; DuFrain, R.J.

    1983-10-01

    In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa(..gamma..d + g(t, tau)d/sup 2/), where t is the time and d is dose. The coefficient of the d/sup 2/ term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure.

  18. Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation.

    Science.gov (United States)

    Harbert, Robert S; Nixon, Kevin C

    2015-08-01

    • Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies. © 2015 Botanical Society of America, Inc.

  19. A Bayes-Maximum Entropy method for multi-sensor data fusion

    Energy Technology Data Exchange (ETDEWEB)

    Beckerman, M.

    1991-01-01

    In this paper we introduce a Bayes-Maximum Entropy formalism for multi-sensor data fusion, and present an application of this methodology to the fusion of ultrasound and visual sensor data as acquired by a mobile robot. In our approach the principle of maximum entropy is applied to the construction of priors and likelihoods from the data. Distances between ultrasound and visual points of interest in a dual representation are used to define Gibbs likelihood distributions. Both one- and two-dimensional likelihoods are presented, and cast into a form which makes explicit their dependence upon the mean. The Bayesian posterior distributions are used to test a null hypothesis, and Maximum Entropy Maps used for navigation are updated using the resulting information from the dual representation. 14 refs., 9 figs.

  20. Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones: A Maximum Likelihood Approach

    Directory of Open Access Journals (Sweden)

    López-Valcarce Roberto

    2004-01-01

    Full Text Available We address the problem of estimating the speed of a road vehicle from its acoustic signature, recorded by a pair of omnidirectional microphones located next to the road. This choice of sensors is motivated by their nonintrusive nature as well as low installation and maintenance costs. A novel estimation technique is proposed, which is based on the maximum likelihood principle. It directly estimates car speed without any assumptions on the acoustic signal emitted by the vehicle. This has the advantages of bypassing troublesome intermediate delay estimation steps as well as eliminating the need for an accurate yet general enough acoustic traffic model. An analysis of the estimate for narrowband and broadband sources is provided and verified with computer simulations. The estimation algorithm uses a bank of modified crosscorrelators and therefore it is well suited to DSP implementation, performing well with preliminary field data.

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

    Directory of Open Access Journals (Sweden)

    Kodner Robin B

    2010-10-01

    Full Text Available Abstract 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.

  2. An Invariance Property for the Maximum Likelihood Estimator of the Parameters of a Gaussian Moving Average Process

    OpenAIRE

    Godolphin, E. J.

    1980-01-01

    It is shown that the estimation procedure of Walker leads to estimates of the parameters of a Gaussian moving average process which are asymptotically equivalent to the maximum likelihood estimates proposed by Whittle and represented by Godolphin.

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

    Science.gov (United States)

    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.

  4. EPR spectrum deconvolution and dose assessment of fossil tooth enamel using maximum likelihood common factor analysis

    International Nuclear Information System (INIS)

    Vanhaelewyn, G.; Callens, F.; Gruen, R.

    2000-01-01

    In order to determine the components which give rise to the EPR spectrum around g = 2 we have applied Maximum Likelihood Common Factor Analysis (MLCFA) on the EPR spectra of enamel sample 1126 which has previously been analysed by continuous wave and pulsed EPR as well as EPR microscopy. MLCFA yielded agreeing results on three sets of X-band spectra and the following components were identified: an orthorhombic component attributed to CO - 2 , an axial component CO 3- 3 , as well as four isotropic components, three of which could be attributed to SO - 2 , a tumbling CO - 2 and a central line of a dimethyl radical. The X-band results were confirmed by analysis of Q-band spectra where three additional isotropic lines were found, however, these three components could not be attributed to known radicals. The orthorhombic component was used to establish dose response curves for the assessment of the past radiation dose, D E . The results appear to be more reliable than those based on conventional peak-to-peak EPR intensity measurements or simple Gaussian deconvolution methods

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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

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

    Science.gov (United States)

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

    2013-12-01

    topography and gravity, in which the INITIAL loading by topography retains the Matern form but the FINAL topography and gravity are the result of flexural compensation. In our modeling, we pay explicit attention to finite-field spectral estimation effects (and their remedy via tapering), and to the implementation of statistical tests (for anisotropy, for initial-loading process correlation, to ascertain the proper density contrasts and interface depth in a two-layer model), robustness assessment and uncertainty quantification, as well as to algorithmic intricacies related to low-dimensional but poorly scaled maximum-likelihood inversions. We conclude that Venusian geomorphic terrains are well described by their 2-D topographic and gravity (cross-)power spectra, and the spectral properties of distinct geologic provinces on Venus are worth quantifying via maximum-likelihood-based methods under idealized three-parameter Matern distributions. Analysis of fitted parameters and the fitted-data residuals reveals natural variability in the (sub)surface properties on Venus, as well as some directional anisotropy. Geologic regions tend to cluster according to terrain type in our parameter space, which we analyze to confirm their shared geologic histories and utilize for guidance in ongoing mapping efforts of Venus and other terrestrial bodies.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  9. A Maximum Likelihood Estimation of Vocal-Tract-Related Filter Characteristics for Single Channel Speech Separation

    Directory of Open Access Journals (Sweden)

    Dansereau Richard M

    2007-01-01

    Full Text Available We present a new technique for separating two speech signals from a single recording. The proposed method bridges the gap between underdetermined blind source separation techniques and those techniques that model the human auditory system, that is, computational auditory scene analysis (CASA. For this purpose, we decompose the speech signal into the excitation signal and the vocal-tract-related filter and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function (PDF of the mixed speech's log spectral vectors in terms of the PDFs of the underlying speech signal's vocal-tract-related filters. Then, the mean vectors of PDFs of the vocal-tract-related filters are obtained using a maximum likelihood estimator given the mixed signal. Finally, the estimated vocal-tract-related filters along with the extracted fundamental frequencies are used to reconstruct estimates of the individual speech signals. The proposed technique effectively adds vocal-tract-related filter characteristics as a new cue to CASA models using a new grouping technique based on an underdetermined blind source separation. We compare our model with both an underdetermined blind source separation and a CASA method. The experimental results show that our model outperforms both techniques in terms of SNR improvement and the percentage of crosstalk suppression.

  10. A Maximum Likelihood Estimation of Vocal-Tract-Related Filter Characteristics for Single Channel Speech Separation

    Directory of Open Access Journals (Sweden)

    Mohammad H. Radfar

    2006-11-01

    Full Text Available We present a new technique for separating two speech signals from a single recording. The proposed method bridges the gap between underdetermined blind source separation techniques and those techniques that model the human auditory system, that is, computational auditory scene analysis (CASA. For this purpose, we decompose the speech signal into the excitation signal and the vocal-tract-related filter and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function (PDF of the mixed speech's log spectral vectors in terms of the PDFs of the underlying speech signal's vocal-tract-related filters. Then, the mean vectors of PDFs of the vocal-tract-related filters are obtained using a maximum likelihood estimator given the mixed signal. Finally, the estimated vocal-tract-related filters along with the extracted fundamental frequencies are used to reconstruct estimates of the individual speech signals. The proposed technique effectively adds vocal-tract-related filter characteristics as a new cue to CASA models using a new grouping technique based on an underdetermined blind source separation. We compare our model with both an underdetermined blind source separation and a CASA method. The experimental results show that our model outperforms both techniques in terms of SNR improvement and the percentage of crosstalk suppression.

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

    Science.gov (United States)

    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.

  12. Comparisons of likelihood and machine learning methods of individual classification

    Science.gov (United States)

    Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.

    2002-01-01

    Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of

  13. ldr: An R Software Package for Likelihood-Based Su?cient Dimension Reduction

    Directory of Open Access Journals (Sweden)

    Kofi Placid Adragni

    2014-11-01

    Full Text Available In regression settings, a su?cient dimension reduction (SDR method seeks the core information in a p-vector predictor that completely captures its relationship with a response. The reduced predictor may reside in a lower dimension d < p, improving ability to visualize data and predict future observations, and mitigating dimensionality issues when carrying out further analysis. We introduce ldr, a new R software package that implements three recently proposed likelihood-based methods for SDR: covariance reduction, likelihood acquired directions, and principal fitted components. All three methods reduce the dimensionality of the data by pro jection into lower dimensional subspaces. The package also implements a variable screening method built upon principal ?tted components which makes use of ?exible basis functions to capture the dependencies between the predictors and the response. Examples are given to demonstrate likelihood-based SDR analyses using ldr, including estimation of the dimension of reduction subspaces and selection of basis functions. The ldr package provides a framework that we hope to grow into a comprehensive library of likelihood-based SDR methodologies.

  14. Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics

    International Nuclear Information System (INIS)

    Prix, Reinhard; Krishnan, Badri

    2009-01-01

    We investigate the Bayesian framework for detection of continuous gravitational waves (GWs) in the context of targeted searches, where the phase evolution of the GW signal is assumed to be known, while the four amplitude parameters are unknown. We show that the orthodox maximum-likelihood statistic (known as F-statistic) can be rediscovered as a Bayes factor with an unphysical prior in amplitude parameter space. We introduce an alternative detection statistic ('B-statistic') using the Bayes factor with a more natural amplitude prior, namely an isotropic probability distribution for the orientation of GW sources. Monte Carlo simulations of targeted searches show that the resulting Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e., has a higher expected detection probability at equal false-alarm probability) than the frequentist F-statistic.

  15. LDR: A Package for Likelihood-Based Sufficient Dimension Reduction

    Directory of Open Access Journals (Sweden)

    R. Dennis Cook

    2011-03-01

    Full Text Available We introduce a new mlab software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.

  16. Maximum Likelihood Blood Velocity Estimator Incorporating Properties of Flow Physics

    DEFF Research Database (Denmark)

    Schlaikjer, Malene; Jensen, Jørgen Arendt

    2004-01-01

    )-data under investigation. The flow physic properties are exploited in the second term, as the range of velocity values investigated in the cross-correlation analysis are compared to the velocity estimates in the temporal and spatial neighborhood of the signal segment under investigation. The new estimator...... has been compared to the cross-correlation (CC) estimator and the previously developed maximum likelihood estimator (MLE). The results show that the CMLE can handle a larger velocity search range and is capable of estimating even low velocity levels from tissue motion. The CC and the MLE produce...... for the CC and the MLE. When the velocity search range is set to twice the limit of the CC and the MLE, the number of incorrect velocity estimates are 0, 19.1, and 7.2% for the CMLE, CC, and MLE, respectively. The ability to handle a larger search range and estimating low velocity levels was confirmed...

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

    Science.gov (United States)

    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).

  18. Maximum likelihood approach to “informed” Sound Source Localization for Hearing Aid applications

    DEFF Research Database (Denmark)

    Farmani, Mojtaba; Pedersen, Michael Syskind; Tan, Zheng-Hua

    2015-01-01

    Most state-of-the-art Sound Source Localization (SSL) algorithms have been proposed for applications which are "uninformed'' about the target sound content; however, utilizing a wireless microphone worn by a target talker, enables recent Hearing Aid Systems (HASs) to access to an almost noise......-free sound signal of the target talker at the HAS via the wireless connection. Therefore, in this paper, we propose a maximum likelihood (ML) approach, which we call MLSSL, to estimate the Direction of Arrival (DoA) of the target signal given access to the target signal content. Compared with other "informed...

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

    Science.gov (United States)

    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/

  20. Density estimation by maximum quantum entropy

    International Nuclear Information System (INIS)

    Silver, R.N.; Wallstrom, T.; Martz, H.F.

    1993-01-01

    A new Bayesian method for non-parametric density estimation is proposed, based on a mathematical analogy to quantum statistical physics. The mathematical procedure is related to maximum entropy methods for inverse problems and image reconstruction. The information divergence enforces global smoothing toward default models, convexity, positivity, extensivity and normalization. The novel feature is the replacement of classical entropy by quantum entropy, so that local smoothing is enforced by constraints on differential operators. The linear response of the estimate is proportional to the covariance. The hyperparameters are estimated by type-II maximum likelihood (evidence). The method is demonstrated on textbook data sets

  1. Use of (D, MUF) and maximum-likelihood methods for detecting falsification and diversion in data-verification problems

    International Nuclear Information System (INIS)

    Goldman, A.S.; Beedgen, R.

    1982-01-01

    The investigation of data falsification and/or diversion is of major concern in nuclear materials accounting procedures used in international safeguards. In this paper, two procedures, denoted by (D,MUF) and LR (Likelihood Ratio), are discussed and compared when testing the hypothesis that neither diversion nor falsification has taken place versus the one-sided alternative that at least one of these parameters is positive. Critical regions and detection probabilities are given for both tests. It is shown that the LR method outperforms (D,MUF) when diversion and falsification take place

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

    Science.gov (United States)

    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.].

  3. Computing maximum likelihood estimates of loglinear models from marginal sums with special attention to loglinear item response theory

    NARCIS (Netherlands)

    Kelderman, Henk

    1992-01-01

    In this paper algorithms are described for obtaining the maximum likelihood estimates of the parameters in loglinear 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

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

    Science.gov (United States)

    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.

  5. Performance and Complexity Analysis of Blind FIR Channel Identification Algorithms Based on Deterministic Maximum Likelihood in SIMO Systems

    DEFF Research Database (Denmark)

    De Carvalho, Elisabeth; Omar, Samir; Slock, Dirk

    2013-01-01

    We analyze two algorithms that have been introduced previously for Deterministic Maximum Likelihood (DML) blind estimation of multiple FIR channels. The first one is a modification of the Iterative Quadratic ML (IQML) algorithm. IQML gives biased estimates of the channel and performs poorly at low...... to the initialization. Its asymptotic performance does not reach the DML performance though. The second strategy, called Pseudo-Quadratic ML (PQML), is naturally denoised. The denoising in PQML is furthermore more efficient than in DIQML: PQML yields the same asymptotic performance as DML, as opposed to DIQML......, but requires a consistent initialization. We furthermore compare DIQML and PQML to the strategy of alternating minimization w.r.t. symbols and channel for solving DML (AQML). An asymptotic performance analysis, a complexity evaluation and simulation results are also presented. The proposed DIQML and PQML...

  6. Computing maximum likelihood estimates of loglinear models from marginal sums with special attention to loglinear item response theory

    NARCIS (Netherlands)

    Kelderman, Henk

    1991-01-01

    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

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

    Science.gov (United States)

    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...

  8. Unbinned maximum likelihood fit for the CP conserving couplings for W + photon production at CDF

    International Nuclear Information System (INIS)

    Lannon, K.

    1994-01-01

    We present an unbinned maximum likelihood fit as an alternative to the currently used fit for the CP conserving couplings W plus photon production studied at CDF. We show that a four parameter double exponential fits the E T spectrum of the photon very well. We also show that the fit parameters can be related to and by a second order polynomial. Finally, we discuss various conclusions we have reasoned from our results to the fit so far

  9. Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging

    Directory of Open Access Journals (Sweden)

    Naoya Sueishi

    2013-07-01

    Full Text Available This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a specific parameter of interest. Then, this study investigates a generalized empirical likelihood-based model averaging estimator that minimizes the asymptotic mean squared error. A simulation study suggests that our averaging estimator can be a useful alternative to existing post-selection estimators.

  10. Effect of indirect dependencies on "Maximum likelihood blind separation of two quantum states (qubits) with cylindrical-symmetry Heisenberg spin coupling"

    OpenAIRE

    Deville, Yannick; Deville, Alain

    2009-01-01

    In a previous paper [1], we investigated the Blind Source Separation (BSS) problem, for the nonlinear mixing model that we introduced in that paper. We proposed to solve this problem by using a maximum likelihood (ML) approach. When applying the ML approach to BSS problems, one usually determines the analytical expressions of the derivatives of the log-likelihood with respect to the parameters of the considered mixing model. In the literature, these calculations were mainly considered for lin...

  11. Maximum likelihood estimation of semiparametric mixture component models for competing risks data.

    Science.gov (United States)

    Choi, Sangbum; Huang, Xuelin

    2014-09-01

    In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma. © 2014, The International Biometric Society.

  12. Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm.

    Science.gov (United States)

    Goolsby, Eric W

    2017-04-01

    Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars

  13. Failed refutations: further comments on parsimony and likelihood methods and their relationship to Popper's degree of corroboration.

    Science.gov (United States)

    de Queiroz, Kevin; Poe, Steven

    2003-06-01

    Kluge's (2001, Syst. Biol. 50:322-330) continued arguments that phylogenetic methods based on the statistical principle of likelihood are incompatible with the philosophy of science described by Karl Popper are based on false premises related to Kluge's misrepresentations of Popper's philosophy. Contrary to Kluge's conjectures, likelihood methods are not inherently verificationist; they do not treat every instance of a hypothesis as confirmation of that hypothesis. The historical nature of phylogeny does not preclude phylogenetic hypotheses from being evaluated using the probability of evidence. The low absolute probabilities of hypotheses are irrelevant to the correct interpretation of Popper's concept termed degree of corroboration, which is defined entirely in terms of relative probabilities. Popper did not advocate minimizing background knowledge; in any case, the background knowledge of both parsimony and likelihood methods consists of the general assumption of descent with modification and additional assumptions that are deterministic, concerning which tree is considered most highly corroborated. Although parsimony methods do not assume (in the sense of entailing) that homoplasy is rare, they do assume (in the sense of requiring to obtain a correct phylogenetic inference) certain things about patterns of homoplasy. Both parsimony and likelihood methods assume (in the sense of implying by the manner in which they operate) various things about evolutionary processes, although violation of those assumptions does not always cause the methods to yield incorrect phylogenetic inferences. Test severity is increased by sampling additional relevant characters rather than by character reanalysis, although either interpretation is compatible with the use of phylogenetic likelihood methods. Neither parsimony nor likelihood methods assess test severity (critical evidence) when used to identify a most highly corroborated tree(s) based on a single method or model and a

  14. A new maximum likelihood blood velocity estimator incorporating spatial and temporal correlation

    DEFF Research Database (Denmark)

    Schlaikjer, Malene; Jensen, Jørgen Arendt

    2001-01-01

    and space. This paper presents a new estimator (STC-MLE), which incorporates the correlation property. It is an expansion of the maximum likelihood estimator (MLE) developed by Ferrara et al. With the MLE a cross-correlation analysis between consecutive RF-lines on complex form is carried out for a range...... of possible velocities. In the new estimator an additional similarity investigation for each evaluated velocity and the available velocity estimates in a temporal (between frames) and spatial (within frames) neighborhood is performed. An a priori probability density term in the distribution...... of the observations gives a probability measure of the correlation between the velocities. Both the MLE and the STC-MLE have been evaluated on simulated and in-vivo RF-data obtained from the carotid artery. Using the MLE 4.1% of the estimates deviate significantly from the true velocities, when the performance...

  15. Analytical maximum-likelihood method to detect patterns in real networks

    International Nuclear Information System (INIS)

    Squartini, Tiziano; Garlaschelli, Diego

    2011-01-01

    In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, the generation of them is still problematic. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.

  16. Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement

    Directory of Open Access Journals (Sweden)

    Siti Tabi'atul Hasanah

    2012-11-01

    Full Text Available Outlier is an observation that much different (extreme from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear regression. Nonlinear regression here is devoted to multiplicative nonlinear regression. To detect is use of statistical method likelihood displacement. Statistical methods abbreviated likelihood displacement (LD is a method to detect outliers by removing the suspected outlier data. To estimate the parameters are used to the maximum likelihood method, so we get the estimate of the maximum. By using LD method is obtained i.e likelihood displacement is thought to contain outliers. Further accuracy of LD method in detecting the outliers are shown by comparing the MSE of LD with the MSE from the regression in general. Statistic test used is Λ. Initial hypothesis was rejected when proved so is an outlier.

  17. Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

    International Nuclear Information System (INIS)

    Stsepankou, D; Arns, A; Hesser, J; Ng, S K; Zygmanski, P

    2012-01-01

    The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone–beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system. (paper)

  18. Two-Stage Maximum Likelihood Estimation (TSMLE for MT-CDMA Signals in the Indoor Environment

    Directory of Open Access Journals (Sweden)

    Sesay Abu B

    2004-01-01

    Full Text Available This paper proposes a two-stage maximum likelihood estimation (TSMLE technique suited for multitone code division multiple access (MT-CDMA system. Here, an analytical framework is presented in the indoor environment for determining the average bit error rate (BER of the system, over Rayleigh and Ricean fading channels. The analytical model is derived for quadrature phase shift keying (QPSK modulation technique by taking into account the number of tones, signal bandwidth (BW, bit rate, and transmission power. Numerical results are presented to validate the analysis, and to justify the approximations made therein. Moreover, these results are shown to agree completely with those obtained by simulation.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Kyungsoo Kim

    2016-06-01

    Full Text Available 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°.

  1. Unbinned likelihood analysis of EGRET observations

    International Nuclear Information System (INIS)

    Digel, Seth W.

    2000-01-01

    We present a newly-developed likelihood analysis method for EGRET data that defines the likelihood function without binning the photon data or averaging the instrumental response functions. The standard likelihood analysis applied to EGRET data requires the photons to be binned spatially and in energy, and the point-spread functions to be averaged over energy and inclination angle. The full-width half maximum of the point-spread function increases by about 40% from on-axis to 30 degree sign inclination, and depending on the binning in energy can vary by more than that in a single energy bin. The new unbinned method avoids the loss of information that binning and averaging cause and can properly analyze regions where EGRET viewing periods overlap and photons with different inclination angles would otherwise be combined in the same bin. In the poster, we describe the unbinned analysis method and compare its sensitivity with binned analysis for detecting point sources in EGRET data

  2. Implementation of non-linear filters for iterative penalized maximum likelihood image reconstruction

    International Nuclear Information System (INIS)

    Liang, Z.; Gilland, D.; Jaszczak, R.; Coleman, R.

    1990-01-01

    In this paper, the authors report on the implementation of six edge-preserving, noise-smoothing, non-linear filters applied in image space for iterative penalized maximum-likelihood (ML) SPECT image reconstruction. The non-linear smoothing filters implemented were the median filter, the E 6 filter, the sigma filter, the edge-line filter, the gradient-inverse filter, and the 3-point edge filter with gradient-inverse filter, and the 3-point edge filter with gradient-inverse weight. A 3 x 3 window was used for all these filters. The best image obtained, by viewing the profiles through the image in terms of noise-smoothing, edge-sharpening, and contrast, was the one smoothed with the 3-point edge filter. The computation time for the smoothing was less than 1% of one iteration, and the memory space for the smoothing was negligible. These images were compared with the results obtained using Bayesian analysis

  3. Implementation of linear filters for iterative penalized maximum likelihood SPECT reconstruction

    International Nuclear Information System (INIS)

    Liang, Z.

    1991-01-01

    This paper reports on six low-pass linear filters applied in frequency space implemented for iterative penalized maximum-likelihood (ML) SPECT image reconstruction. The filters implemented were the Shepp-Logan filter, the Butterworth filer, the Gaussian filter, the Hann filter, the Parzen filer, and the Lagrange filter. The low-pass filtering was applied in frequency space to projection data for the initial estimate and to the difference of projection data and reprojected data for higher order approximations. The projection data were acquired experimentally from a chest phantom consisting of non-uniform attenuating media. All the filters could effectively remove the noise and edge artifacts associated with ML approach if the frequency cutoff was properly chosen. The improved performance of the Parzen and Lagrange filters relative to the others was observed. The best image, by viewing its profiles in terms of noise-smoothing, edge-sharpening, and contrast, was the one obtained with the Parzen filter. However, the Lagrange filter has the potential to consider the characteristics of detector response function

  4. A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

    KAUST Repository

    Lee, Seokho

    2013-01-31

    We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a simple bicluster structure and the combination of multiple layers is able to reveal complicated, multiple biclusters. The method allows for non-pure biclusters, and can simultaneously identify the 1-prevalent blocks and 0-prevalent blocks. A computationally efficient algorithm is developed and guidelines are provided for specifying the tuning parameters, including initial values of model parameters, the number of layers, and the penalty parameters. Missing-data imputation can be handled in the EM framework. The method is tested using synthetic and real datasets and shows good performance. © 2013 Springer Science+Business Media New York.

  5. [DIN-compatible vision assessment of increased reproducibility using staircase measurement and maximum likelihood analysis].

    Science.gov (United States)

    Weigmann, U; Petersen, J

    1996-08-01

    Visual acuity determination according to DIN 58,220 does not make full use of the information received about the patient, in contrast to the staircase method. Thus, testing the same number of optotypes, the staircase method should yield more reproducible acuity results. On the other hand, the staircase method gives systematically higher acuity values because it converges on the 48% point of the psychometric function (for Landolt rings in eight positions) and not on the 65% probability, as DIN 58,220 with criterion 3/5 does. This bias can be avoided by means of a modified evaluation. Using the staircase data we performed a maximum likelihood estimate of the psychometric function as a whole and computed the acuity value for 65% probability of correct answers. We determined monocular visual acuity in 102 persons with widely differing visual performance. Each subject underwent four tests in random order, two according to DIN 58,220 and two using the modified staircase method (Landolt rings in eight positions scaled by a factor 1.26; PC monitor with 1024 x 768 pixels; distance 4.5 m). Each test was performed with 25 optotypes. The two procedures provide the same mean visual acuity values (difference less than 0.02 acuity steps). The test-retest results match in 30.4% of DIN repetitions but in 50% of the staircases. The standard deviation of the test-retest difference is 1.41 (DIN) and 1.06 (modified staircase) acuity steps. Thus the standard deviation of the single test is 1.0 (DIN) and 0.75 (modified staircase) acuity steps. The new method provides visual acuity values identical to DIN 58,220 but is superior with respect to reproducibility.

  6. Estimation of stochastic frontier models with fixed-effects through Monte Carlo Maximum Likelihood

    NARCIS (Netherlands)

    Emvalomatis, G.; Stefanou, S.E.; Oude Lansink, A.G.J.M.

    2011-01-01

    Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are

  7. FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods

    Directory of Open Access Journals (Sweden)

    Bakos Jason D

    2010-04-01

    Full Text Available Abstract Background Likelihood (ML-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data. This method is used in applications such as RAxML, GARLI, MrBayes, PAML, and PAUP. The Phylogenetic Likelihood Function (PLF is an important kernel computation for this method. The PLF consists of a loop with no conditional behavior or dependencies between iterations. As such it contains a high potential for exploiting parallelism using micro-architectural techniques. In this paper, we describe a technique for mapping the PLF and supporting logic onto a Field Programmable Gate Array (FPGA-based co-processor. By leveraging the FPGA's on-chip DSP modules and the high-bandwidth local memory attached to the FPGA, the resultant co-processor can accelerate ML-based methods and outperform state-of-the-art multi-core processors. Results We use the MrBayes 3 tool as a framework for designing our co-processor. For large datasets, we estimate that our accelerated MrBayes, if run on a current-generation FPGA, achieves a 10× speedup relative to software running on a state-of-the-art server-class microprocessor. The FPGA-based implementation achieves its performance by deeply pipelining the likelihood computations, performing multiple floating-point operations in parallel, and through a natural log approximation that is chosen specifically to leverage a deeply pipelined custom architecture. Conclusions Heterogeneous computing, which combines general-purpose processors with special-purpose co-processors such as FPGAs and GPUs, is a promising approach for high-performance phylogeny inference as shown by the growing body of literature in this field. FPGAs in particular are well-suited for this task because of their low power consumption as compared to many-core processors and Graphics Processor Units (GPUs 1.

  8. Extended maximum likelihood analysis of apparent flattenings of S0 and spiral galaxies

    International Nuclear Information System (INIS)

    Okamura, Sadanori; Takase, Bunshiro; Hamabe, Masaru; Nakada, Yoshikazu; Kodaira, Keiichi.

    1981-01-01

    Apparent flattenings of S0 and spiral galaxies compiled by Sandage et al. (1970) and van den Bergh (1977), and those listed in the Second Reference Catalogue (RC2) are analyzed by means of the extended maximum likelihood method which was recently developed in the information theory for statistical model identification. Emphasis is put on the possible difference in the distribution of intrinsic flattenings between S0's and spirals as a group, and on the apparent disagreements present in the previous results. The present analysis shows that (1) One cannot conclude on the basis of the data in the Reference Catalogue of Bright Galaxies (RCBG) that the distribution of intrinsic flattenings of spirals is almost identical to that of S0's; spirals have wider dispersion than S0's, and there are more round systems in spirals than in S0's. (2) The distribution of intrinsic flattenings of S0's and spirals derived from the data in RC2 again indicates a significant difference from each other. (3) The distribution of intrinsic flattenings of S0's exhibits different characteristics depending upon the surface-brightness level; the distribution with one component is obtained from the data at RCBG level (--23.5 mag arcsec -2 ) and that with two components at RC2 level (25 mag arcsec -2 ). (author)

  9. Constraint likelihood analysis for a network of gravitational wave detectors

    International Nuclear Information System (INIS)

    Klimenko, S.; Rakhmanov, M.; Mitselmakher, G.; Mohanty, S.

    2005-01-01

    We propose a coherent method for detection and reconstruction of gravitational wave signals with a network of interferometric detectors. The method is derived by using the likelihood ratio functional for unknown signal waveforms. In the likelihood analysis, the global maximum of the likelihood ratio over the space of waveforms is used as the detection statistic. We identify a problem with this approach. In the case of an aligned pair of detectors, the detection statistic depends on the cross correlation between the detectors as expected, but this dependence disappears even for infinitesimally small misalignments. We solve the problem by applying constraints on the likelihood functional and obtain a new class of statistics. The resulting method can be applied to data from a network consisting of any number of detectors with arbitrary detector orientations. The method allows us reconstruction of the source coordinates and the waveforms of two polarization components of a gravitational wave. We study the performance of the method with numerical simulations and find the reconstruction of the source coordinates to be more accurate than in the standard likelihood method

  10. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisová, Katarina

    To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  11. Practical likelihood analysis for spatial generalized linear mixed models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Ribeiro, Paulo Justiniano

    2016-01-01

    We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

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

    Science.gov (United States)

    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

  13. %lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models

    Directory of Open Access Journals (Sweden)

    Maja Olsbjerg

    2015-10-01

    Full Text Available Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed and used within educational research, they are also used when focus is on physical functioning or psychological wellbeing. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a SAS macro that fits two-dimensional polytomous Rasch models using a specification of the model that is sufficiently flexible to accommodate longitudinal Rasch models. The macro estimates item parameters using marginal maximum likelihood estimation. A graphical presentation of item characteristic curves is included.

  14. Maximum likelihood inference of small trees in the presence of long branches.

    Science.gov (United States)

    Parks, Sarah L; Goldman, Nick

    2014-09-01

    The statistical basis of maximum likelihood (ML), its robustness, and the fact that it appears to suffer less from biases lead to it being one of the most popular methods for tree reconstruction. Despite its popularity, very few analytical solutions for ML exist, so biases suffered by ML are not well understood. One possible bias is long branch attraction (LBA), a regularly cited term generally used to describe a propensity for long branches to be joined together in estimated trees. Although initially mentioned in connection with inconsistency of parsimony, LBA has been claimed to affect all major phylogenetic reconstruction methods, including ML. Despite the widespread use of this term in the literature, exactly what LBA is and what may be causing it is poorly understood, even for simple evolutionary models and small model trees. Studies looking at LBA have focused on the effect of two long branches on tree reconstruction. However, to understand the effect of two long branches it is also important to understand the effect of just one long branch. If ML struggles to reconstruct one long branch, then this may have an impact on LBA. In this study, we look at the effect of one long branch on three-taxon tree reconstruction. We show that, counterintuitively, long branches are preferentially placed at the tips of the tree. This can be understood through the use of analytical solutions to the ML equation and distance matrix methods. We go on to look at the placement of two long branches on four-taxon trees, showing that there is no attraction between long branches, but that for extreme branch lengths long branches are joined together disproportionally often. These results illustrate that even small model trees are still interesting to help understand how ML phylogenetic reconstruction works, and that LBA is a complicated phenomenon that deserves further study. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  15. Efficient Detection of Repeating Sites to Accelerate Phylogenetic Likelihood Calculations.

    Science.gov (United States)

    Kobert, K; Stamatakis, A; Flouri, T

    2017-03-01

    The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the PLF currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation. [Algorithms; maximum likelihood; phylogenetic likelihood function; phylogenetics]. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  16. Analysis of Pairwise Interactions in a Maximum Likelihood Sense to Identify Leaders in a Group

    Directory of Open Access Journals (Sweden)

    Violet Mwaffo

    2017-07-01

    Full Text Available Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader–follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader–follower relationships.

  17. Likelihood-ratio-based biometric verification

    NARCIS (Netherlands)

    Bazen, A.M.; Veldhuis, Raymond N.J.

    2002-01-01

    This paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that for single-user verification the likelihood ratio is optimal.

  18. Likelihood Ratio-Based Biometric Verification

    NARCIS (Netherlands)

    Bazen, A.M.; Veldhuis, Raymond N.J.

    The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal.

  19. PROCOV: maximum likelihood estimation of protein phylogeny under covarion models and site-specific covarion pattern analysis

    Directory of Open Access Journals (Sweden)

    Wang Huai-Chun

    2009-09-01

    Full Text Available Abstract Background The covarion hypothesis of molecular evolution holds that selective pressures on a given amino acid or nucleotide site are dependent on the identity of other sites in the molecule that change throughout time, resulting in changes of evolutionary rates of sites along the branches of a phylogenetic tree. At the sequence level, covarion-like evolution at a site manifests as conservation of nucleotide or amino acid states among some homologs where the states are not conserved in other homologs (or groups of homologs. Covarion-like evolution has been shown to relate to changes in functions at sites in different clades, and, if ignored, can adversely affect the accuracy of phylogenetic inference. Results PROCOV (protein covarion analysis is a software tool that implements a number of previously proposed covarion models of protein evolution for phylogenetic inference in a maximum likelihood framework. Several algorithmic and implementation improvements in this tool over previous versions make computationally expensive tree searches with covarion models more efficient and analyses of large phylogenomic data sets tractable. PROCOV can be used to identify covarion sites by comparing the site likelihoods under the covarion process to the corresponding site likelihoods under a rates-across-sites (RAS process. Those sites with the greatest log-likelihood difference between a 'covarion' and an RAS process were found to be of functional or structural significance in a dataset of bacterial and eukaryotic elongation factors. Conclusion Covarion models implemented in PROCOV may be especially useful for phylogenetic estimation when ancient divergences between sequences have occurred and rates of evolution at sites are likely to have changed over the tree. It can also be used to study lineage-specific functional shifts in protein families that result in changes in the patterns of site variability among subtrees.

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

    Science.gov (United States)

    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.

  1. Adjusted Empirical Likelihood Method in the Presence of Nuisance Parameters with Application to the Sharpe Ratio

    Directory of Open Access Journals (Sweden)

    Yuejiao Fu

    2018-04-01

    Full Text Available The Sharpe ratio is a widely used risk-adjusted performance measurement in economics and finance. Most of the known statistical inferential methods devoted to the Sharpe ratio are based on the assumption that the data are normally distributed. In this article, without making any distributional assumption on the data, we develop the adjusted empirical likelihood method to obtain inference for a parameter of interest in the presence of nuisance parameters. We show that the log adjusted empirical likelihood ratio statistic is asymptotically distributed as the chi-square distribution. The proposed method is applied to obtain inference for the Sharpe ratio. Simulation results illustrate that the proposed method is comparable to Jobson and Korkie’s method (1981 and outperforms the empirical likelihood method when the data are from a symmetric distribution. In addition, when the data are from a skewed distribution, the proposed method significantly outperforms all other existing methods. A real-data example is analyzed to exemplify the application of the proposed method.

  2. Efficient simulation and likelihood methods for non-neutral multi-allele models.

    Science.gov (United States)

    Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

    2012-06-01

    Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

  3. Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

    KAUST Repository

    Yan, Yuan; Genton, Marc G.

    2017-01-01

    Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.

  4. Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

    KAUST Repository

    Yan, Yuan

    2017-07-13

    Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.

  5. Maximum Quantum Entropy Method

    OpenAIRE

    Sim, Jae-Hoon; Han, Myung Joon

    2018-01-01

    Maximum entropy method for analytic continuation is extended by introducing quantum relative entropy. This new method is formulated in terms of matrix-valued functions and therefore invariant under arbitrary unitary transformation of input matrix. As a result, the continuation of off-diagonal elements becomes straightforward. Without introducing any further ambiguity, the Bayesian probabilistic interpretation is maintained just as in the conventional maximum entropy method. The applications o...

  6. Assessing Compatibility of Direct Detection Data: Halo-Independent Global Likelihood Analyses

    CERN Document Server

    Gelmini, Graciela B.

    2016-10-18

    We present two different halo-independent methods utilizing a global maximum likelihood that can assess the compatibility of dark matter direct detection data given a particular dark matter model. The global likelihood we use is comprised of at least one extended likelihood and an arbitrary number of Poisson or Gaussian likelihoods. In the first method we find the global best fit halo function and construct a two sided pointwise confidence band, which can then be compared 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 $p \\geq 10\\%$). For any halo function not entirely contained within the plausibility region, the level of compatibility of the data is very low (e.g. $p < 10 \\%$). As an example we apply these methods to CDMS-II-Si and SuperCDMS data, assuming dark matter particles with elastic s...

  7. Evaluation of Smoking Prevention Television Messages Based on the Elaboration Likelihood Model

    Science.gov (United States)

    Flynn, Brian S.; Worden, John K.; Bunn, Janice Yanushka; Connolly, Scott W.; Dorwaldt, Anne L.

    2011-01-01

    Progress in reducing youth smoking may depend on developing improved methods to communicate with higher risk youth. This study explored the potential of smoking prevention messages based on the Elaboration Likelihood Model (ELM) to address these needs. Structured evaluations of 12 smoking prevention messages based on three strategies derived from…

  8. A note on estimating errors from the likelihood function

    International Nuclear Information System (INIS)

    Barlow, Roger

    2005-01-01

    The points at which the log likelihood falls by 12 from its maximum value are often used to give the 'errors' on a result, i.e. the 68% central confidence interval. The validity of this is examined for two simple cases: a lifetime measurement and a Poisson measurement. Results are compared with the exact Neyman construction and with the simple Bartlett approximation. It is shown that the accuracy of the log likelihood method is poor, and the Bartlett construction explains why it is flawed

  9. Bayesian and maximum likelihood estimation of genetic maps

    DEFF Research Database (Denmark)

    York, Thomas L.; Durrett, Richard T.; Tanksley, Steven

    2005-01-01

    There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods...... of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances....

  10. Energy-Based Acoustic Source Localization Methods: A Survey

    Directory of Open Access Journals (Sweden)

    Wei Meng

    2017-02-01

    Full Text Available Energy-based source localization is an important problem in wireless sensor networks (WSNs, which has been studied actively in the literature. Numerous localization algorithms, e.g., maximum likelihood estimation (MLE and nonlinear-least-squares (NLS methods, have been reported. In the literature, there are relevant review papers for localization in WSNs, e.g., for distance-based localization. However, not much work related to energy-based source localization is covered in the existing review papers. Energy-based methods are proposed and specially designed for a WSN due to its limited sensor capabilities. This paper aims to give a comprehensive review of these different algorithms for energy-based single and multiple source localization problems, their merits and demerits and to point out possible future research directions.

  11. Analytical method for optimization of maintenance policy based on available system failure data

    International Nuclear Information System (INIS)

    Coria, V.H.; Maximov, S.; Rivas-Dávalos, F.; Melchor, C.L.; Guardado, J.L.

    2015-01-01

    An analytical optimization method for preventive maintenance (PM) policy with minimal repair at failure, periodic maintenance, and replacement is proposed for systems with historical failure time data influenced by a current PM policy. The method includes a new imperfect PM model based on Weibull distribution and incorporates the current maintenance interval T 0 and the optimal maintenance interval T to be found. The Weibull parameters are analytically estimated using maximum likelihood estimation. Based on this model, the optimal number of PM and the optimal maintenance interval for minimizing the expected cost over an infinite time horizon are also analytically determined. A number of examples are presented involving different failure time data and current maintenance intervals to analyze how the proposed analytical optimization method for periodic PM policy performances in response to changes in the distribution of the failure data and the current maintenance interval. - Highlights: • An analytical optimization method for preventive maintenance (PM) policy is proposed. • A new imperfect PM model is developed. • The Weibull parameters are analytically estimated using maximum likelihood. • The optimal maintenance interval and number of PM are also analytically determined. • The model is validated by several numerical examples

  12. Philosophy and phylogenetic inference: a comparison of likelihood and parsimony methods in the context of Karl Popper's writings on corroboration.

    Science.gov (United States)

    de Queiroz, K; Poe, S

    2001-06-01

    Advocates of cladistic parsimony methods have invoked the philosophy of Karl Popper in an attempt to argue for the superiority of those methods over phylogenetic methods based on Ronald Fisher's statistical principle of likelihood. We argue that the concept of likelihood in general, and its application to problems of phylogenetic inference in particular, are highly compatible with Popper's philosophy. Examination of Popper's writings reveals that his concept of corroboration is, in fact, based on likelihood. Moreover, because probabilistic assumptions are necessary for calculating the probabilities that define Popper's corroboration, likelihood methods of phylogenetic inference--with their explicit probabilistic basis--are easily reconciled with his concept. In contrast, cladistic parsimony methods, at least as described by certain advocates of those methods, are less easily reconciled with Popper's concept of corroboration. If those methods are interpreted as lacking probabilistic assumptions, then they are incompatible with corroboration. Conversely, if parsimony methods are to be considered compatible with corroboration, then they must be interpreted as carrying implicit probabilistic assumptions. Thus, the non-probabilistic interpretation of cladistic parsimony favored by some advocates of those methods is contradicted by an attempt by the same authors to justify parsimony methods in terms of Popper's concept of corroboration. In addition to being compatible with Popperian corroboration, the likelihood approach to phylogenetic inference permits researchers to test the assumptions of their analytical methods (models) in a way that is consistent with Popper's ideas about the provisional nature of background knowledge.

  13. Direct reconstruction of the source intensity distribution of a clinical linear accelerator using a maximum likelihood expectation maximization algorithm.

    Science.gov (United States)

    Papaconstadopoulos, P; Levesque, I R; Maglieri, R; Seuntjens, J

    2016-02-07

    Direct determination of the source intensity distribution of clinical linear accelerators is still a challenging problem for small field beam modeling. Current techniques most often involve special equipment and are difficult to implement in the clinic. In this work we present a maximum-likelihood expectation-maximization (MLEM) approach to the source reconstruction problem utilizing small fields and a simple experimental set-up. The MLEM algorithm iteratively ray-traces photons from the source plane to the exit plane and extracts corrections based on photon fluence profile measurements. The photon fluence profiles were determined by dose profile film measurements in air using a high density thin foil as build-up material and an appropriate point spread function (PSF). The effect of other beam parameters and scatter sources was minimized by using the smallest field size ([Formula: see text] cm(2)). The source occlusion effect was reproduced by estimating the position of the collimating jaws during this process. The method was first benchmarked against simulations for a range of typical accelerator source sizes. The sources were reconstructed with an accuracy better than 0.12 mm in the full width at half maximum (FWHM) to the respective electron sources incident on the target. The estimated jaw positions agreed within 0.2 mm with the expected values. The reconstruction technique was also tested against measurements on a Varian Novalis Tx linear accelerator and compared to a previously commissioned Monte Carlo model. The reconstructed FWHM of the source agreed within 0.03 mm and 0.11 mm to the commissioned electron source in the crossplane and inplane orientations respectively. The impact of the jaw positioning, experimental and PSF uncertainties on the reconstructed source distribution was evaluated with the former presenting the dominant effect.

  14. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisova, K.

    2010-01-01

    This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  15. Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood

    Directory of Open Access Journals (Sweden)

    Yunquan Song

    2014-01-01

    Full Text Available Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.

  16. Novel TPPO Based Maximum Power Point Method for Photovoltaic System

    Directory of Open Access Journals (Sweden)

    ABBASI, M. A.

    2017-08-01

    Full Text Available Photovoltaic (PV system has a great potential and it is installed more when compared with other renewable energy sources nowadays. However, the PV system cannot perform optimally due to its solid reliance on climate conditions. Due to this dependency, PV system does not operate at its maximum power point (MPP. Many MPP tracking methods have been proposed for this purpose. One of these is the Perturb and Observe Method (P&O which is the most famous due to its simplicity, less cost and fast track. But it deviates from MPP in continuously changing weather conditions, especially in rapidly changing irradiance conditions. A new Maximum Power Point Tracking (MPPT method, Tetra Point Perturb and Observe (TPPO, has been proposed to improve PV system performance in changing irradiance conditions and the effects on characteristic curves of PV array module due to varying irradiance are delineated. The Proposed MPPT method has shown better results in increasing the efficiency of a PV system.

  17. Modelling maximum river flow by using Bayesian Markov Chain Monte Carlo

    Science.gov (United States)

    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.

  18. On the performance of social network and likelihood-based expert weighting schemes

    International Nuclear Information System (INIS)

    Cooke, Roger M.; ElSaadany, Susie; Huang Xinzheng

    2008-01-01

    Using expert judgment data from the TU Delft's expert judgment database, we compare the performance of different weighting schemes, namely equal weighting, performance-based weighting from the classical model [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991.], social network (SN) weighting and likelihood weighting. The picture that emerges with regard to SN weights is rather mixed. SN theory does not provide an alternative to performance-based combination of expert judgments, since the statistical accuracy of the SN decision maker is sometimes unacceptably low. On the other hand, it does outperform equal weighting in the majority of cases. The results here, though not overwhelmingly positive, do nonetheless motivate further research into social interaction methods for nominating and weighting experts. Indeed, a full expert judgment study with performance measurement requires an investment in time and effort, with a view to securing external validation. If high confidence in a comparable level of validation can be obtained by less intensive methods, this would be very welcome, and would facilitate the application of structured expert judgment in situations where the resources for a full study are not available. Likelihood weights are just as resource intensive as performance-based weights, and the evidence presented here suggests that they are inferior to performance-based weights with regard to those scoring variables which are optimized in performance weights (calibration and information). Perhaps surprisingly, they are also inferior with regard to likelihood. Their use is further discouraged by the fact that they constitute a strongly improper scoring rule

  19. Maximum entropy analysis of EGRET data

    DEFF Research Database (Denmark)

    Pohl, M.; Strong, A.W.

    1997-01-01

    EGRET data are usually analysed on the basis of the Maximum-Likelihood method \\cite{ma96} in a search for point sources in excess to a model for the background radiation (e.g. \\cite{hu97}). This method depends strongly on the quality of the background model, and thus may have high systematic unce...... uncertainties in region of strong and uncertain background like the Galactic Center region. Here we show images of such regions obtained by the quantified Maximum-Entropy method. We also discuss a possible further use of MEM in the analysis of problematic regions of the sky....

  20. Empirical likelihood

    CERN Document Server

    Owen, Art B

    2001-01-01

    Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling.One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer vi...

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

    Science.gov (United States)

    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.

  2. FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses.

    Directory of Open Access Journals (Sweden)

    Maxim Nikolaievich Shokhirev

    Full Text Available The immune response is a concerted dynamic multi-cellular process. Upon infection, the dynamics of lymphocyte populations are an aggregate of molecular processes that determine the activation, division, and longevity of individual cells. The timing of these single-cell processes is remarkably widely distributed with some cells undergoing their third division while others undergo their first. High cell-to-cell variability and technical noise pose challenges for interpreting popular dye-dilution experiments objectively. It remains an unresolved challenge to avoid under- or over-interpretation of such data when phenotyping gene-targeted mouse models or patient samples. Here we develop and characterize a computational methodology to parameterize a cell population model in the context of noisy dye-dilution data. To enable objective interpretation of model fits, our method estimates fit sensitivity and redundancy by stochastically sampling the solution landscape, calculating parameter sensitivities, and clustering to determine the maximum-likelihood solution ranges. Our methodology accounts for both technical and biological variability by using a cell fluorescence model as an adaptor during population model fitting, resulting in improved fit accuracy without the need for ad hoc objective functions. We have incorporated our methodology into an integrated phenotyping tool, FlowMax, and used it to analyze B cells from two NFκB knockout mice with distinct phenotypes; we not only confirm previously published findings at a fraction of the expended effort and cost, but reveal a novel phenotype of nfkb1/p105/50 in limiting the proliferative capacity of B cells following B-cell receptor stimulation. In addition to complementing experimental work, FlowMax is suitable for high throughput analysis of dye dilution studies within clinical and pharmacological screens with objective and quantitative conclusions.

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

    Science.gov (United States)

    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.

  4. 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.

    Science.gov (United States)

    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

  5. Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

    Science.gov (United States)

    Wang, Z.

    2015-12-01

    For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

  6. Composite likelihood estimation of demographic parameters

    Directory of Open Access Journals (Sweden)

    Garrigan Daniel

    2009-11-01

    Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable

  7. Development of likelihood estimation method for criticality accidents of mixed oxide fuel fabrication facilities

    International Nuclear Information System (INIS)

    Tamaki, Hitoshi; Yoshida, Kazuo; Kimoto, Tatsuya; Hamaguchi, Yoshikane

    2010-01-01

    A criticality accident in a MOX fuel fabrication facility may occur depending on several parameters, such as mass inventory and plutonium enrichment. MOX handling units in the facility are designed and operated based on the double contingency principle to prevent criticality accidents. Control failures of at least two parameters are needed for the occurrence of criticality accident. To evaluate the probability of such control failures, the criticality conditions of each parameter for a specific handling unit are necessary for accident scenario analysis to be clarified quantitatively with a criticality analysis computer code. In addition to this issue, a computer-based control system for mass inventory is planned to be installed into MOX handling equipment in a commercial MOX fuel fabrication plant. The reliability analysis is another important issue in evaluating the likelihood of control failure caused by software malfunction. A likelihood estimation method for criticality accident has been developed with these issues been taken into consideration. In this paper, an example of analysis with the proposed method and the applicability of the method are also shown through a trial application to a model MOX fabrication facility. (author)

  8. Analysis of the maximum likelihood channel estimator for OFDM systems in the presence of unknown interference

    Science.gov (United States)

    Dermoune, Azzouz; Simon, Eric Pierre

    2017-12-01

    This paper is a theoretical analysis of the maximum likelihood (ML) channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in the presence of unknown interference. The following theoretical results are presented. Firstly, the uniqueness of the ML solution for practical applications, i.e., when thermal noise is present, is analytically demonstrated when the number of transmitted OFDM symbols is strictly greater than one. The ML solution is then derived from the iterative conditional ML (CML) algorithm. Secondly, it is shown that the channel estimate can be described as an algebraic function whose inputs are the initial value and the means and variances of the received samples. Thirdly, it is theoretically demonstrated that the channel estimator is not biased. The second and the third results are obtained by employing oblique projection theory. Furthermore, these results are confirmed by numerical results.

  9. Simulation-based marginal likelihood for cluster strong lensing cosmology

    Science.gov (United States)

    Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.

    2018-01-01

    Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.

  10. Likelihood ratio meta-analysis: New motivation and approach for an old method.

    Science.gov (United States)

    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.

  11. On the Quirks of Maximum Parsimony and Likelihood on Phylogenetic Networks

    OpenAIRE

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

    2015-01-01

    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 phylogene...

  12. Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

    NARCIS (Netherlands)

    Ramos, Daniel; Haraksim, Rudolf; Meuwly, Didier

    2017-01-01

    Data to which the authors refer to throughout this article are likelihood ratios (LR) computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR) method in forensic evidence evaluation. These data present a

  13. Weighted profile likelihood-based confidence interval for the difference between two proportions with paired binomial data.

    Science.gov (United States)

    Pradhan, Vivek; Saha, Krishna K; Banerjee, Tathagata; Evans, John C

    2014-07-30

    Inference on the difference between two binomial proportions in the paired binomial setting is often an important problem in many biomedical investigations. Tang et al. (2010, Statistics in Medicine) discussed six methods to construct confidence intervals (henceforth, we abbreviate it as CI) for the difference between two proportions in paired binomial setting using method of variance estimates recovery. In this article, we propose weighted profile likelihood-based CIs for the difference between proportions of a paired binomial distribution. However, instead of the usual likelihood, we use weighted likelihood that is essentially making adjustments to the cell frequencies of a 2 × 2 table in the spirit of Agresti and Min (2005, Statistics in Medicine). We then conduct numerical studies to compare the performances of the proposed CIs with that of Tang et al. and Agresti and Min in terms of coverage probabilities and expected lengths. Our numerical study clearly indicates that the weighted profile likelihood-based intervals and Jeffreys interval (cf. Tang et al.) are superior in terms of achieving the nominal level, and in terms of expected lengths, they are competitive. Finally, we illustrate the use of the proposed CIs with real-life examples. Copyright © 2014 John Wiley & Sons, Ltd.

  14. Probabilistic properties of the date of maximum river flow, an approach based on circular statistics in lowland, highland and mountainous catchment

    Science.gov (United States)

    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.

  15. Assessing compatibility of direct detection data: halo-independent global likelihood analyses

    Energy Technology Data Exchange (ETDEWEB)

    Gelmini, Graciela B. [Department of Physics and Astronomy, UCLA,475 Portola Plaza, Los Angeles, CA 90095 (United States); Huh, Ji-Haeng [CERN Theory Division,CH-1211, Geneva 23 (Switzerland); Witte, Samuel J. [Department of Physics and Astronomy, UCLA,475 Portola Plaza, Los Angeles, CA 90095 (United States)

    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 compared 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.

  16. Methods for estimating the semivariogram

    DEFF Research Database (Denmark)

    Lophaven, Søren Nymand; Carstensen, Niels Jacob; Rootzen, Helle

    2002-01-01

    . In the existing literature various methods for modelling the semivariogram have been proposed, while only a few studies have been made on comparing different approaches. In this paper we compare eight approaches for modelling the semivariogram, i.e. six approaches based on least squares estimation...... maximum likelihood performed better than the least squares approaches. We also applied maximum likelihood and least squares estimation to a real dataset, containing measurements of salinity at 71 sampling stations in the Kattegat basin. This showed that the calculation of spatial predictions...

  17. PERBANDINGAN ESTIMASI KEMAMPUAN LATEN ANTARA METODE MAKSIMUM LIKELIHOOD DAN METODE BAYES

    Directory of Open Access Journals (Sweden)

    Heri Retnawati

    2015-10-01

    Full Text Available Studi ini bertujuan untuk membandingkan ketepatan estimasi kemampuan laten (latent trait pada model logistik dengan metode maksimum likelihood (ML gabungan dan bayes. Studi ini menggunakan metode simulasi Monte Carlo, dengan model data ujian nasional matematika SMP. Variabel simulasi adalah panjang tes dan banyaknya peserta.  Data dibangkitkan dengan menggunakan SAS/IML dengan replikasi 40 kali, dan tiap data diestimasi dengan ML dan Bayes. Hasil estimasi kemudian dibandingkan dengan kemampuan yang sebenarnya, dengan menghitung mean square of error (MSE dan korelasi antara kemampuan laten yang sebenarnya dan hasil estimasi. Metode yang memiliki MSE lebih kecil dikatakan sebagai metode estimasi yang lebih baik. Hasil studi menunjukkan bahwa pada estimasi kemampuan laten dengan 15, 20, 25, dan 30 butir dengan 500 dan 1.000 peserta, hasil MSE belum stabil, namun ketika peserta menjadi 1.500 orang, diperoleh akurasi estimasi kemampuan yang hampir sama baik estimasi antara metode ML dan metode Bayes. Pada estimasi dengan 15 dan 20 butir dan peserta 500, 1.000, dan 1.500, hasil MSE belum stabil, dan ketika estimasi melibatkan 25 dan 30 butir, baik dengan peserta 500, 1.000, maupun 1.500 akan diperoleh hasil yang lebih akurat dengan metode ML. Kata kunci: estimasi kemampuan, metode maksimum likelihood, metode Bayes     THE COMPARISON OF ESTIMATION OF LATENT TRAITS USING MAXIMUM LIKELIHOOD AND BAYES METHODS Abstract This study aimed to compare the accuracy of the estimation of latent ability (latent trait in the logistic model using maximum likelihood (ML and Bayes methods. This study uses a quantitative approach that is the Monte Carlo simulation method using students responses to national examination as data model, and variables are the length of the test and the number of participants. The data were generated using SAS/IML with replication 40 times, and each datum is then estimated by ML and Bayes. The estimation results are then compared with the

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

    Science.gov (United States)

    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.

  19. Mathematical methods for B0 anti B0 oscillation analyses

    International Nuclear Information System (INIS)

    Moser, H.G.; Roussarie, A.

    1996-01-01

    The measurement of the B 0 s anti B 0 s mixing frequency Δm s requires the search for a periodic pattern in the time distribution of the data. Using Fourier analysis the consequences of vertex and boost resolution, mistag and statistical fluctuations are treated analytically and a general expression to estimate the significance of a B 0 anti B 0 mixing analysis is derived. With the help of Fourier analysis the behaviour of a classical maximum likelihood analysis in time space is studied, too. It can be shown that a naive maximum likelihood fit fails in general to give correct confidence levels. This is especially important if limits are calculated. Alternative methods, based on the likelihood, which give correct limits are discussed. A new method, the amplitude fit, is introduced which combines the advantages of a Fourier analysis with the power and simplicity of a maximum likelihood fit. (orig.)

  20. Quasi-Maximum Likelihood Estimation and Bootstrap Inference in Fractional Time Series Models with Heteroskedasticity of Unknown Form

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Nielsen, Morten Ørregaard; Taylor, Robert

    We consider the problem of conducting estimation and inference on the parameters of univariate heteroskedastic fractionally integrated time series models. We first extend existing results in the literature, developed for conditional sum-of squares estimators in the context of parametric fractional...... time series models driven by conditionally homoskedastic shocks, to allow for conditional and unconditional heteroskedasticity both of a quite general and unknown form. Global consistency and asymptotic normality are shown to still obtain; however, the covariance matrix of the limiting distribution...... of the estimator now depends on nuisance parameters derived both from the weak dependence and heteroskedasticity present in the shocks. We then investigate classical methods of inference based on the Wald, likelihood ratio and Lagrange multiplier tests for linear hypotheses on either or both of the long and short...

  1. Maximum-Entropy Inference with a Programmable Annealer

    Science.gov (United States)

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

    2016-03-01

    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.

  2. A New Online Calibration Method Based on Lord's Bias-Correction.

    Science.gov (United States)

    He, Yinhong; Chen, Ping; Li, Yong; Zhang, Shumei

    2017-09-01

    Online calibration technique has been widely employed to calibrate new items due to its advantages. Method A is the simplest online calibration method and has attracted many attentions from researchers recently. However, a key assumption of Method A is that it treats person-parameter estimates θ ^ s (obtained by maximum likelihood estimation [MLE]) as their true values θ s , thus the deviation of the estimated θ ^ s from their true values might yield inaccurate item calibration when the deviation is nonignorable. To improve the performance of Method A, a new method, MLE-LBCI-Method A, is proposed. This new method combines a modified Lord's bias-correction method (named as maximum likelihood estimation-Lord's bias-correction with iteration [MLE-LBCI]) with the original Method A in an effort to correct the deviation of θ ^ s which may adversely affect the item calibration precision. Two simulation studies were carried out to explore the performance of both MLE-LBCI and MLE-LBCI-Method A under several scenarios. Simulation results showed that MLE-LBCI could make a significant improvement over the ML ability estimates, and MLE-LBCI-Method A did outperform Method A in almost all experimental conditions.

  3. Matrix-based image reconstruction methods for tomography

    International Nuclear Information System (INIS)

    Llacer, J.; Meng, J.D.

    1984-10-01

    Matrix methods of image reconstruction have not been used, in general, because of the large size of practical matrices, ill condition upon inversion and the success of Fourier-based techniques. An exception is the work that has been done at the Lawrence Berkeley Laboratory for imaging with accelerated radioactive ions. An extension of that work into more general imaging problems shows that, with a correct formulation of the problem, positron tomography with ring geometries results in well behaved matrices which can be used for image reconstruction with no distortion of the point response in the field of view and flexibility in the design of the instrument. Maximum Likelihood Estimator methods of reconstruction, which use the system matrices tailored to specific instruments and do not need matrix inversion, are shown to result in good preliminary images. A parallel processing computer structure based on multiple inexpensive microprocessors is proposed as a system to implement the matrix-MLE methods. 14 references, 7 figures

  4. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  5. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  6. Profile-likelihood Confidence Intervals in Item Response Theory Models.

    Science.gov (United States)

    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.

  7. Afrika Statistika ISSN 2316-090X Comparison of the maximum ...

    African Journals Online (AJOL)

    †Badji-Mokhtar University Department of Mathematics B.P.12, Annaba 23000. Algeria. ‡Laboratory of ... Using the maximum likelihood method and the Bayesian approach, we estimate the parameters and ...... Japan Statist. Soc. 14. 145-155.

  8. Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

    Energy Technology Data Exchange (ETDEWEB)

    Price, Oliver R., E-mail: oliver.price@unilever.co [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Oliver, Margaret A. [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Walker, Allan [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); Wood, Martin [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom)

    2009-05-15

    An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.

  9. Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

    International Nuclear Information System (INIS)

    Price, Oliver R.; Oliver, Margaret A.; Walker, Allan; Wood, Martin

    2009-01-01

    An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.

  10. A Review of Methods for Missing Data.

    Science.gov (United States)

    Pigott, Therese D.

    2001-01-01

    Reviews methods for handling missing data in a research study. Model-based methods, such as maximum likelihood using the EM algorithm and multiple imputation, hold more promise than ad hoc methods. Although model-based methods require more specialized computer programs and assumptions about the nature of missing data, these methods are appropriate…

  11. Predicting Porosity and Permeability for the Canyon Formation, SACROC Unit (Kelly-Snyder Field), Using the Geologic Analysis via Maximum Likelihood System

    International Nuclear Information System (INIS)

    Reinaldo Gonzalez; Scott R. Reeves; Eric Eslinger

    2007-01-01

    , with high vertical resolution, could be generated for many wells. This procedure permits to populate any well location with core-scale estimates of P and P and rock types facilitating the application of geostatistical characterization methods. The first step procedure was to discriminate rock types of similar depositional environment and/or reservoir quality (RQ) using a specific clustering technique. The approach implemented utilized a model-based, probabilistic clustering analysis procedure called GAMLS1,2,3,4 (Geologic Analysis via Maximum Likelihood System) which is based on maximum likelihood principles. During clustering, samples (data at each digitized depth from each well) are probabilistically assigned to a previously specified number of clusters with a fractional probability that varies between zero and one

  12. Average Likelihood Methods of Classification of Code Division Multiple Access (CDMA)

    Science.gov (United States)

    2016-05-01

    subject to code matrices that follows the structure given by (113). [⃗ yR y⃗I ] = √ Es 2L [ GR1 −GI1 GI2 GR2 ] [ QR −QI QI QR ] [⃗ bR b⃗I ] + [⃗ nR n⃗I... QR ] [⃗ b+ b⃗− ] + [⃗ n+ n⃗− ] (115) The average likelihood for type 4 CDMA (116) is a special case of type 1 CDMA with twice the code length and...AVERAGE LIKELIHOOD METHODS OF CLASSIFICATION OF CODE DIVISION MULTIPLE ACCESS (CDMA) MAY 2016 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE

  13. New BFA Method Based on Attractor Neural Network and Likelihood Maximization

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.; Snášel, V.

    2014-01-01

    Roč. 132, 20 May (2014), s. 14-29 ISSN 0925-2312 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional support: RVO:67985807 Keywords : recurrent neural network * associative memory * Hebbian learning rule * neural network application * data mining * statistics * Boolean factor analysis * information gain * dimension reduction * likelihood-maximization * bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014

  14. A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

    NARCIS (Netherlands)

    Meuwly, Didier; Ramos, Daniel; Haraksim, Rudolf

    2017-01-01

    This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes’ inference model. In the context of the inference of identity of source, the Likelihood Ratio is used to evaluate the strength of

  15. Measurement of the top quark mass with the dynamical likelihood method using lepton plus jets events with b-tags in p anti-p collisions at s**(1/2) = 1.96-TeV

    Energy Technology Data Exchange (ETDEWEB)

    Abulencia, A.; Acosta, D.; Adelman, Jahred A.; Affolder, Anthony A.; Akimoto, T.; Albrow, M.G.; Ambrose, D.; Amerio, S.; Amidei, D.; Anastassov, A.; Anikeev, K.; /Taiwan,

    2005-12-01

    This report describes a measurement of the top quark mass, M{sub top}, with the dynamical likelihood method (DLM) using the CDF II detector at the Fermilab Tevatron. The Tevatron produces top/anti-top (t{bar t}) pairs in p{bar p} collisions at a center-of-mass energy of 1.96 TeV. The data sample used in this analysis was accumulated from March 2002 through August 2004, which corresponds to an integrated luminosity of 318 pb{sup -1}. They use the t{bar t} candidates in the ''lepton+jets'' decay channel, requiring at least one jet identified as a b quark by finding an displaced secondary vertex. The DLM defines a likelihood for each event based on the differential cross section as a function of M{sub top} per unit phase space volume of the final partons, multiplied by the transfer functions from jet to parton energies. The method takes into account all possible jet combinations in an event, and the likelihood is multiplied event by event to derive the top quark mass by the maximum likelihood method. Using 63 t{bar t} candidates observed in the data, with 9.2 events expected from background, they measure the top quark mass to be 173.2{sub -2.4}{sup +2.6}(stat.) {+-} 3.2(syst.) GeV/c{sup 2}, or 173.2{sub -4.0}{sup +4.1} GeV/c{sup 2}.

  16. The effect of the neutron spectra unfolding method on the fast neutron dose determination

    International Nuclear Information System (INIS)

    Marinkovic, P.; Zavaljevski, N.

    1992-01-01

    Based on Shanon's information theory, a new unfolding method which gives non-negative spectrum values and a relatively low variance, is proposed, and a numerical code suitable for application in fast neutron spectroscopy based on proton recoil is developed. The principles of maximum entropy and maximum likelihood are jointly applied. According to the principle of maximum likelihood, the distribution functions around the mean value of the counts in the MCA channels are assumed to be Gaussians. The Lagrange parameter method is applied in the search for an optimal non-negative solution. The nonlinear system of equations is solved using the gradient and Newton iterative algorithms. (orig.)

  17. The effect of the neutron spectra unfolding method on the fast neutron dose determination

    International Nuclear Information System (INIS)

    Marinkovic, P.; Avdic, S.; Pesic, M.; Zavaljevski, N

    1992-09-01

    Based on Shanon's information theory, a new unfolding method which gives non-negative spectrum values and a relatively low variance, is proposed, and a numerical code suitable for application in fast neutron spectroscopy based on proton recoil is developed. The principles of maximum entropy and maximum likelihood are jointly applied. According to the principle of maximum likelihood, the distribution functions around the mean value of the counts in the MCA channels are assumed to be Gaussians. The Lagrange parameter method is applied in the search for an optimal non-negative solution. The nonlinear system of equations is solved using the gradient and Newton iterative algorithms. (author)

  18. Determination of the maximum-depth to potential field sources by a maximum structural index method

    Science.gov (United States)

    Fedi, M.; Florio, G.

    2013-01-01

    A simple and fast determination of the limiting depth to the sources may represent a significant help to the data interpretation. To this end we explore the possibility of determining those source parameters shared by all the classes of models fitting the data. One approach is to determine the maximum depth-to-source compatible with the measured data, by using for example the well-known Bott-Smith rules. These rules involve only the knowledge of the field and its horizontal gradient maxima, and are independent from the density contrast. Thanks to the direct relationship between structural index and depth to sources we work out a simple and fast strategy to obtain the maximum depth by using the semi-automated methods, such as Euler deconvolution or depth-from-extreme-points method (DEXP). The proposed method consists in estimating the maximum depth as the one obtained for the highest allowable value of the structural index (Nmax). Nmax may be easily determined, since it depends only on the dimensionality of the problem (2D/3D) and on the nature of the analyzed field (e.g., gravity field or magnetic field). We tested our approach on synthetic models against the results obtained by the classical Bott-Smith formulas and the results are in fact very similar, confirming the validity of this method. However, while Bott-Smith formulas are restricted to the gravity field only, our method is applicable also to the magnetic field and to any derivative of the gravity and magnetic field. Our method yields a useful criterion to assess the source model based on the (∂f/∂x)max/fmax ratio. The usefulness of the method in real cases is demonstrated for a salt wall in the Mississippi basin, where the estimation of the maximum depth agrees with the seismic information.

  19. A Hybrid Maximum Power Point Tracking Method for Automobile Exhaust Thermoelectric Generator

    Science.gov (United States)

    Quan, Rui; Zhou, Wei; Yang, Guangyou; Quan, Shuhai

    2017-05-01

    To make full use of the maximum output power of automobile exhaust thermoelectric generator (AETEG) based on Bi2Te3 thermoelectric modules (TEMs), taking into account the advantages and disadvantages of existing maximum power point tracking methods, and according to the output characteristics of TEMs, a hybrid maximum power point tracking method combining perturb and observe (P&O) algorithm, quadratic interpolation and constant voltage tracking method was put forward in this paper. Firstly, it searched the maximum power point with P&O algorithms and a quadratic interpolation method, then, it forced the AETEG to work at its maximum power point with constant voltage tracking. A synchronous buck converter and controller were implemented in the electric bus of the AETEG applied in a military sports utility vehicle, and the whole system was modeled and simulated with a MATLAB/Simulink environment. Simulation results demonstrate that the maximum output power of the AETEG based on the proposed hybrid method is increased by about 3.0% and 3.7% compared with that using only the P&O algorithm and the quadratic interpolation method, respectively. The shorter tracking time is only 1.4 s, which is reduced by half compared with that of the P&O algorithm and quadratic interpolation method, respectively. The experimental results demonstrate that the tracked maximum power is approximately equal to the real value using the proposed hybrid method,and it can preferentially deal with the voltage fluctuation of the AETEG with only P&O algorithm, and resolve the issue that its working point can barely be adjusted only with constant voltage tracking when the operation conditions change.

  20. Maximum likelihood and Bayesian analyses of a combined nucleotide sequence dataset for genetic characterization of a novel pestivirus, SVA/cont-08.

    Science.gov (United States)

    Liu, Lihong; Xia, Hongyan; Baule, Claudia; Belák, Sándor

    2009-01-01

    Bovine viral diarrhoea virus 1 (BVDV-1) and Bovine viral diarrhoea virus 2 (BVDV-2) are two recognised bovine pestivirus species of the genus Pestivirus. Recently, a pestivirus, termed SVA/cont-08, was detected in a batch of contaminated foetal calf serum originating from South America. Comparative sequence analysis showed that the SVA/cont-08 virus shares 15-28% higher sequence identity to pestivirus D32/00_'HoBi' than to members of BVDV-1 and BVDV-2. In order to reveal the phylogenetic relationship of SVA/cont-08 with other pestiviruses, a molecular dataset of 30 pestiviruses and 1,896 characters, comprising the 5'UTR, N(pro) and E2 gene regions, was analysed by two methods: maximum likelihood and Bayesian approach. An identical, well-supported tree topology was observed, where four pestiviruses (SVA/cont-08, D32/00_'HoBi', CH-KaHo/cont, and Th/04_KhonKaen) formed a monophyletic clade that is closely related to the BVDV-1 and BVDV-2 clades. The strategy applied in this study is useful for classifying novel pestiviruses in the future.

  1. A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data

    KAUST Repository

    Liang, Faming

    2013-03-01

    The Gaussian geostatistical model has been widely used in modeling of spatial data. However, it is challenging to computationally implement this method because it requires the inversion of a large covariance matrix, particularly when there is a large number of observations. This article proposes a resampling-based stochastic approximation method to address this challenge. At each iteration of the proposed method, a small subsample is drawn from the full dataset, and then the current estimate of the parameters is updated accordingly under the framework of stochastic approximation. Since the proposed method makes use of only a small proportion of the data at each iteration, it avoids inverting large covariance matrices and thus is scalable to large datasets. The proposed method also leads to a general parameter estimation approach, maximum mean log-likelihood estimation, which includes the popular maximum (log)-likelihood estimation (MLE) approach as a special case and is expected to play an important role in analyzing large datasets. Under mild conditions, it is shown that the estimator resulting from the proposed method converges in probability to a set of parameter values of equivalent Gaussian probability measures, and that the estimator is asymptotically normally distributed. To the best of the authors\\' knowledge, the present study is the first one on asymptotic normality under infill asymptotics for general covariance functions. The proposed method is illustrated with large datasets, both simulated and real. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  2. A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT

    Science.gov (United States)

    Wang, Yingmei; Wang, Ge; Mao, Shuwei; Cong, Wenxiang; Ji, Zhilong; Cai, Jian-Feng; Ye, Yangbo

    2016-11-01

    Standard computed tomography (CT) cannot reproduce spectral information of an object. Hardware solutions include dual-energy CT which scans the object twice in different x-ray energy levels, and energy-discriminative detectors which can separate lower and higher energy levels from a single x-ray scan. In this paper, we propose a software solution and give an iterative algorithm that reconstructs an image with spectral information from just one scan with a standard energy-integrating detector. The spectral information obtained can be used to produce color CT images, spectral curves of the attenuation coefficient μ (r,E) at points inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. Our software solution requires no change on hardware of a CT machine. With the Shepp-Logan phantom, we have found that although the photoelectric and Compton components were not perfectly reconstructed, their composite effect was very accurately reconstructed as compared to the ground truth and the dual-energy CT counterpart. This means that our proposed method has an intrinsic benefit in beam hardening correction and metal artifact reduction. The algorithm is based on a nonlinear polychromatic acquisition model for x-ray CT. The key technique is a sparse representation of iterations in a framelet system. Convergence of the algorithm is studied. This is believed to be the first application of framelet imaging tools to a nonlinear inverse problem.

  3. Maximum power point tracker based on fuzzy logic

    International Nuclear Information System (INIS)

    Daoud, A.; Midoun, A.

    2006-01-01

    The solar energy is used as power source in photovoltaic power systems and the need for an intelligent power management system is important to obtain the maximum power from the limited solar panels. With the changing of the sun illumination due to variation of angle of incidence of sun radiation and of the temperature of the panels, Maximum Power Point Tracker (MPPT) enables optimization of solar power generation. The MPPT is a sub-system designed to extract the maximum power from a power source. In the case of solar panels power source. the maximum power point varies as a result of changes in its electrical characteristics which in turn are functions of radiation dose, temperature, ageing and other effects. The MPPT maximum the power output from panels for a given set of conditions by detecting the best working point of the power characteristic and then controls the current through the panels or the voltage across them. Many MPPT methods have been reported in literature. These techniques of MPPT can be classified into three main categories that include: lookup table methods, hill climbing methods and computational methods. The techniques vary according to the degree of sophistication, processing time and memory requirements. The perturbation and observation algorithm (hill climbing technique) is commonly used due to its ease of implementation, and relative tracking efficiency. However, it has been shown that when the insolation changes rapidly, the perturbation and observation method is slow to track the maximum power point. In recent years, the fuzzy controllers are used for maximum power point tracking. This method only requires the linguistic control rules for maximum power point, the mathematical model is not required and therefore the implementation of this control method is easy to real control system. In this paper, we we present a simple robust MPPT using fuzzy set theory where the hardware consists of the microchip's microcontroller unit control card and

  4. Maximum entropy deconvolution of low count nuclear medicine images

    International Nuclear Information System (INIS)

    McGrath, D.M.

    1998-12-01

    Maximum entropy is applied to the problem of deconvolving nuclear medicine images, with special consideration for very low count data. The physics of the formation of scintigraphic images is described, illustrating the phenomena which degrade planar estimates of the tracer distribution. Various techniques which are used to restore these images are reviewed, outlining the relative merits of each. The development and theoretical justification of maximum entropy as an image processing technique is discussed. Maximum entropy is then applied to the problem of planar deconvolution, highlighting the question of the choice of error parameters for low count data. A novel iterative version of the algorithm is suggested which allows the errors to be estimated from the predicted Poisson mean values. This method is shown to produce the exact results predicted by combining Poisson statistics and a Bayesian interpretation of the maximum entropy approach. A facility for total count preservation has also been incorporated, leading to improved quantification. In order to evaluate this iterative maximum entropy technique, two comparable methods, Wiener filtering and a novel Bayesian maximum likelihood expectation maximisation technique, were implemented. The comparison of results obtained indicated that this maximum entropy approach may produce equivalent or better measures of image quality than the compared methods, depending upon the accuracy of the system model used. The novel Bayesian maximum likelihood expectation maximisation technique was shown to be preferable over many existing maximum a posteriori methods due to its simplicity of implementation. A single parameter is required to define the Bayesian prior, which suppresses noise in the solution and may reduce the processing time substantially. Finally, maximum entropy deconvolution was applied as a pre-processing step in single photon emission computed tomography reconstruction of low count data. Higher contrast results were

  5. 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.

    Science.gov (United States)

    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.

  6. Quasi-Maximum-Likelihood Detector Based on Geometrical Diversification Greedy Intensification

    OpenAIRE

    Nafkha , Amor; Boutillon , Emmanuel; Roland , Christian

    2009-01-01

    IEEE Transactions On Communications, vol. 57, n°4, pp. 926-929, April 2009.; International audience; The ML detection used in many communication applications reduces down to solving an integer least-squares problem, which minimizes an objective function. If the matrix is not orthogonal, this problem is NP-hard. An exhaustive search over all feasible solutions is thus only applicable when the dimension of the problem is low. In the literature, there are two main available classes of methods. T...

  7. 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.

    Science.gov (United States)

    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 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.

  8. Extended likelihood inference in reliability

    International Nuclear Information System (INIS)

    Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.

    1978-10-01

    Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist

  9. Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting

    NARCIS (Netherlands)

    Jungbacker, B.M.J.P.; Koopman, S.J.

    2015-01-01

    We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to

  10. Generic maximum likely scale selection

    DEFF Research Database (Denmark)

    Pedersen, Kim Steenstrup; Loog, Marco; Markussen, Bo

    2007-01-01

    in this work is on applying this selection principle under a Brownian image model. This image model provides a simple scale invariant prior for natural images and we provide illustrative examples of the behavior of our scale estimation on such images. In these illustrative examples, estimation is based......The fundamental problem of local scale selection is addressed by means of a novel principle, which is based on maximum likelihood estimation. The principle is generally applicable to a broad variety of image models and descriptors, and provides a generic scale estimation methodology. The focus...

  11. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

    Science.gov (United States)

    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

  12. A fast pulse phase estimation method for X-ray pulsar signals based on epoch folding

    Directory of Open Access Journals (Sweden)

    Xue Mengfan

    2016-06-01

    Full Text Available X-ray pulsar-based navigation (XPNAV is an attractive method for autonomous deep-space navigation in the future. The pulse phase estimation is a key task in XPNAV and its accuracy directly determines the navigation accuracy. State-of-the-art pulse phase estimation techniques either suffer from poor estimation accuracy, or involve the maximization of generally non-convex object function, thus resulting in a large computational cost. In this paper, a fast pulse phase estimation method based on epoch folding is presented. The statistical properties of the observed profile obtained through epoch folding are developed. Based on this, we recognize the joint probability distribution of the observed profile as the likelihood function and utilize a fast Fourier transform-based procedure to estimate the pulse phase. Computational complexity of the proposed estimator is analyzed as well. Experimental results show that the proposed estimator significantly outperforms the currently used cross-correlation (CC and nonlinear least squares (NLS estimators, while significantly reduces the computational complexity compared with NLS and maximum likelihood (ML estimators.

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

    Science.gov (United States)

    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.

  14. MLE [Maximum Likelihood Estimator] reconstruction of a brain phantom using a Monte Carlo transition matrix and a statistical stopping rule

    International Nuclear Information System (INIS)

    Veklerov, E.; Llacer, J.; Hoffman, E.J.

    1987-10-01

    In order to study properties of the Maximum Likelihood Estimator (MLE) algorithm for image reconstruction in Positron Emission Tomographyy (PET), the algorithm is applied to data obtained by the ECAT-III tomograph from a brain phantom. The procedure for subtracting accidental coincidences from the data stream generated by this physical phantom is such that he resultant data are not Poisson distributed. This makes the present investigation different from other investigations based on computer-simulated phantoms. It is shown that the MLE algorithm is robust enough to yield comparatively good images, especially when the phantom is in the periphery of the field of view, even though the underlying assumption of the algorithm is violated. Two transition matrices are utilized. The first uses geometric considerations only. The second is derived by a Monte Carlo simulation which takes into account Compton scattering in the detectors, positron range, etc. in the detectors. It is demonstrated that the images obtained from the Monte Carlo matrix are superior in some specific ways. A stopping rule derived earlier and allowing the user to stop the iterative process before the images begin to deteriorate is tested. Since the rule is based on the Poisson assumption, it does not work well with the presently available data, although it is successful wit computer-simulated Poisson data

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

    Science.gov (United States)

    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).

  16. Maximum likelihood estimation of dose-response parameters for therapeutic operating characteristic (TOC) analysis of carcinoma of the nasopharynx

    International Nuclear Information System (INIS)

    Metz, C.E.; Tokars, R.P.; Kronman, H.B.; Griem, M.L.

    1982-01-01

    A Therapeutic Operating Characteristic (TOC) curve for radiation therapy plots, for all possible treatment doses, the probability of tumor ablation as a function of the probability of radiation-induced complication. Application of this analysis to actual therapeutic situation requires that dose-response curves for ablation and for complication be estimated from clinical data. We describe an approach in which ''maximum likelihood estimates'' of these dose-response curves are made, and we apply this approach to data collected on responses to radiotherapy for carcinoma of the nasopharynx. TOC curves constructed from the estimated dose-response curves are subject to moderately large uncertainties because of the limitations of available data.These TOC curves suggest, however, that treatment doses greater than 1800 rem may substantially increase the probability of tumor ablation with little increase in the risk of radiation-induced cervical myelopathy, especially for T1 and T2 tumors

  17. Maximum entropy methods

    International Nuclear Information System (INIS)

    Ponman, T.J.

    1984-01-01

    For some years now two different expressions have been in use for maximum entropy image restoration and there has been some controversy over which one is appropriate for a given problem. Here two further entropies are presented and it is argued that there is no single correct algorithm. The properties of the four different methods are compared using simple 1D simulations with a view to showing how they can be used together to gain as much information as possible about the original object. (orig.)

  18. Precise charge density studies by maximum entropy method

    CERN Document Server

    Takata, M

    2003-01-01

    For the production research and development of nanomaterials, their structural information is indispensable. Recently, a sophisticated analytical method, which is based on information theory, the Maximum Entropy Method (MEM) using synchrotron radiation powder data, has been successfully applied to determine precise charge densities of metallofullerenes and nanochannel microporous compounds. The results revealed various endohedral natures of metallofullerenes and one-dimensional array formation of adsorbed gas molecules in nanochannel microporous compounds. The concept of MEM analysis was also described briefly. (author)

  19. Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

    Science.gov (United States)

    Wang, Qi; Wang, Huaxiang; Cui, Ziqiang; Yang, Chengyi

    2012-11-01

    Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan

    2014-05-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

  1. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan; Genton, Marc G.

    2014-01-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

  2. Maximum Likelihood based comparison of the specific growth rates for P. aeruginosa and four mutator strains

    DEFF Research Database (Denmark)

    Philipsen, Kirsten Riber; Christiansen, Lasse Engbo; Mandsberg, Lotte Frigaard

    2008-01-01

    with an exponentially decaying function of the time between observations is suggested. A model with a full covariance structure containing OD-dependent variance and an autocorrelation structure is compared to a model with variance only and with no variance or correlation implemented. It is shown that the model...... are used for parameter estimation. The data is log-transformed such that a linear model can be applied. The transformation changes the variance structure, and hence an OD-dependent variance is implemented in the model. The autocorrelation in the data is demonstrated, and a correlation model...... that best describes data is a model taking into account the full covariance structure. An inference study is made in order to determine whether the growth rate of the five bacteria strains is the same. After applying a likelihood-ratio test to models with a full covariance structure, it is concluded...

  3. A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

    Science.gov (United States)

    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.

  4. An improved image non-blind image deblurring method based on FoEs

    Science.gov (United States)

    Zhu, Qidan; Sun, Lei

    2013-03-01

    Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction algorithm that minimizes the KL divergence between the reference distribution and the prior distribution. The approximate KL penalty can restrain over-smooth caused by MAP. We use three groups of images and Harris corner detection to prove our method. The experimental results show that our algorithm of non-blind image restoration can effectively reduce the ringing effect and exhibit the state-of-the-art deblurring results.

  5. On the likelihood function of Gaussian max-stable processes

    KAUST Repository

    Genton, M. G.; Ma, Y.; Sang, H.

    2011-01-01

    We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.

  6. On the likelihood function of Gaussian max-stable processes

    KAUST Repository

    Genton, M. G.

    2011-05-24

    We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.

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

    Energy Technology Data Exchange (ETDEWEB)

    Hogden, J.

    1996-11-05

    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 may 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.

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

    Science.gov (United States)

    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.

  9. Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods.

    Science.gov (United States)

    Towers, Sherry; Mubayi, Anuj; Castillo-Chavez, Carlos

    2018-01-01

    When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences. In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical

  10. Evaluating score- and feature-based likelihood ratio models for multivariate continuous data: applied to forensic MDMA comparison

    NARCIS (Netherlands)

    Bolck, A.; Ni, H.; Lopatka, M.

    2015-01-01

    Likelihood ratio (LR) models are moving into the forefront of forensic evidence evaluation as these methods are adopted by a diverse range of application areas in forensic science. We examine the fundamentally different results that can be achieved when feature- and score-based methodologies are

  11. The phylogenetic likelihood library.

    Science.gov (United States)

    Flouri, T; Izquierdo-Carrasco, F; Darriba, D; Aberer, A J; Nguyen, L-T; Minh, B Q; Von Haeseler, A; Stamatakis, A

    2015-03-01

    We introduce the Phylogenetic Likelihood Library (PLL), a highly optimized application programming interface for developing likelihood-based phylogenetic inference and postanalysis software. The PLL implements appropriate data structures and functions that allow users to quickly implement common, error-prone, and labor-intensive tasks, such as likelihood calculations, model parameter as well as branch length optimization, and tree space exploration. The highly optimized and parallelized implementation of the phylogenetic likelihood function and a thorough documentation provide a framework for rapid development of scalable parallel phylogenetic software. By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2-10 while requiring only 1 month of programming time for integration. We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE. On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required). The PLL is available at http://www.libpll.org under the GNU General Public License (GPL). © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  12. Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

    Energy Technology Data Exchange (ETDEWEB)

    Gang, Grace J. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (Canada); Stayman, J. Webster; Zbijewski, Wojciech [Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (United States); Siewerdsen, Jeffrey H., E-mail: jeff.siewerdsen@jhu.edu [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 (United States)

    2014-08-15

    Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP

  13. A Nuclear Ribosomal DNA Phylogeny of Acer Inferred with Maximum Likelihood, Splits Graphs, and Motif Analysis of 606 Sequences

    Directory of Open Access Journals (Sweden)

    Guido W. Grimm

    2006-01-01

    Full Text Available The multi-copy internal transcribed spacer (ITS region of nuclear ribosomal DNA is widely used to infer phylogenetic relationships among closely related taxa. Here we use maximum likelihood (ML and splits graph analyses to extract phylogenetic information from ~ 600 mostly cloned ITS sequences, representing 81 species and subspecies of Acer, and both species of its sister Dipteronia. Additional analyses compared sequence motifs in Acer and several hundred Anacardiaceae, Burseraceae, Meliaceae, Rutaceae, and Sapindaceae ITS sequences in GenBank. We also assessed the effects of using smaller data sets of consensus sequences with ambiguity coding (accounting for within-species variation instead of the full (partly redundant original sequences. Neighbor-nets and bipartition networks were used to visualize conflict among character state patterns. Species clusters observed in the trees and networks largely agree with morphology-based classifications; of de Jong’s (1994 16 sections, nine are supported in neighbor-net and bipartition networks, and ten by sequence motifs and the ML tree; of his 19 series, 14 are supported in networks, motifs, and the ML tree. Most nodes had higher bootstrap support with matrices of 105 or 40 consensus sequences than with the original matrix. Within-taxon ITS divergence did not differ between diploid and polyploid Acer, and there was little evidence of differentiated parental ITS haplotypes, suggesting that concerted evolution in Acer acts rapidly.

  14. A Nuclear Ribosomal DNA Phylogeny of Acer Inferred with Maximum Likelihood, Splits Graphs, and Motif Analysis of 606 Sequences

    Science.gov (United States)

    Grimm, Guido W.; Renner, Susanne S.; Stamatakis, Alexandros; Hemleben, Vera

    2007-01-01

    The multi-copy internal transcribed spacer (ITS) region of nuclear ribosomal DNA is widely used to infer phylogenetic relationships among closely related taxa. Here we use maximum likelihood (ML) and splits graph analyses to extract phylogenetic information from ~ 600 mostly cloned ITS sequences, representing 81 species and subspecies of Acer, and both species of its sister Dipteronia. Additional analyses compared sequence motifs in Acer and several hundred Anacardiaceae, Burseraceae, Meliaceae, Rutaceae, and Sapindaceae ITS sequences in GenBank. We also assessed the effects of using smaller data sets of consensus sequences with ambiguity coding (accounting for within-species variation) instead of the full (partly redundant) original sequences. Neighbor-nets and bipartition networks were used to visualize conflict among character state patterns. Species clusters observed in the trees and networks largely agree with morphology-based classifications; of de Jong’s (1994) 16 sections, nine are supported in neighbor-net and bipartition networks, and ten by sequence motifs and the ML tree; of his 19 series, 14 are supported in networks, motifs, and the ML tree. Most nodes had higher bootstrap support with matrices of 105 or 40 consensus sequences than with the original matrix. Within-taxon ITS divergence did not differ between diploid and polyploid Acer, and there was little evidence of differentiated parental ITS haplotypes, suggesting that concerted evolution in Acer acts rapidly. PMID:19455198

  15. An alternative empirical likelihood method in missing response problems and causal inference.

    Science.gov (United States)

    Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao

    2016-11-30

    Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. ROC [Receiver Operating Characteristics] study of maximum likelihood estimator human brain image reconstructions in PET [Positron Emission Tomography] clinical practice

    International Nuclear Information System (INIS)

    Llacer, J.; Veklerov, E.; Nolan, D.; Grafton, S.T.; Mazziotta, J.C.; Hawkins, R.A.; Hoh, C.K.; Hoffman, E.J.

    1990-10-01

    This paper will report on the progress to date in carrying out Receiver Operating Characteristics (ROC) studies comparing Maximum Likelihood Estimator (MLE) and Filtered Backprojection (FBP) reconstructions of normal and abnormal human brain PET data in a clinical setting. A previous statistical study of reconstructions of the Hoffman brain phantom with real data indicated that the pixel-to-pixel standard deviation in feasible MLE images is approximately proportional to the square root of the number of counts in a region, as opposed to a standard deviation which is high and largely independent of the number of counts in FBP. A preliminary ROC study carried out with 10 non-medical observers performing a relatively simple detectability task indicates that, for the majority of observers, lower standard deviation translates itself into a statistically significant detectability advantage in MLE reconstructions. The initial results of ongoing tests with four experienced neurologists/nuclear medicine physicians are presented. Normal cases of 18 F -- fluorodeoxyglucose (FDG) cerebral metabolism studies and abnormal cases in which a variety of lesions have been introduced into normal data sets have been evaluated. We report on the results of reading the reconstructions of 90 data sets, each corresponding to a single brain slice. It has become apparent that the design of the study based on reading single brain slices is too insensitive and we propose a variation based on reading three consecutive slices at a time, rating only the center slice. 9 refs., 2 figs., 1 tab

  17. Maximum super angle optimization method for array antenna pattern synthesis

    DEFF Research Database (Denmark)

    Wu, Ji; Roederer, A. G

    1991-01-01

    Different optimization criteria related to antenna pattern synthesis are discussed. Based on the maximum criteria and vector space representation, a simple and efficient optimization method is presented for array and array fed reflector power pattern synthesis. A sector pattern synthesized by a 2...

  18. Maximum parsimony, substitution model, and probability phylogenetic trees.

    Science.gov (United States)

    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.

  19. A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

    Science.gov (United States)

    Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin

    2014-01-01

    Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874

  20. Maximum length scale in density based topology optimization

    DEFF Research Database (Denmark)

    Lazarov, Boyan Stefanov; Wang, Fengwen

    2017-01-01

    The focus of this work is on two new techniques for imposing maximum length scale in topology optimization. Restrictions on the maximum length scale provide designers with full control over the optimized structure and open possibilities to tailor the optimized design for broader range...... of manufacturing processes by fulfilling the associated technological constraints. One of the proposed methods is based on combination of several filters and builds on top of the classical density filtering which can be viewed as a low pass filter applied to the design parametrization. The main idea...

  1. Logic of likelihood

    International Nuclear Information System (INIS)

    Wall, M.J.W.

    1992-01-01

    The notion of open-quotes probabilityclose quotes is generalized to that of open-quotes likelihood,close quotes and a natural logical structure is shown to exist for any physical theory which predicts likelihoods. Two physically based axioms are given for this logical structure to form an orthomodular poset, with an order-determining set of states. The results strengthen the basis of the quantum logic approach to axiomatic quantum theory. 25 refs

  2. A comparison of multidimensional scaling methods for perceptual mapping

    NARCIS (Netherlands)

    Bijmolt, T.H.A.; Wedel, M.

    Multidimensional scaling has been applied to a wide range of marketing problems, in particular to perceptual mapping based on dissimilarity judgments. The introduction of methods based on the maximum likelihood principle is one of the most important developments. In this article, the authors compare

  3. Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

    Science.gov (United States)

    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.

  4. Maximum likely scale estimation

    DEFF Research Database (Denmark)

    Loog, Marco; Pedersen, Kim Steenstrup; Markussen, Bo

    2005-01-01

    A maximum likelihood local scale estimation principle is presented. An actual implementation of the estimation principle uses second order moments of multiple measurements at a fixed location in the image. These measurements consist of Gaussian derivatives possibly taken at several scales and/or ...

  5. A new maximum power point method based on a sliding mode approach for solar energy harvesting

    International Nuclear Information System (INIS)

    Farhat, Maissa; Barambones, Oscar; Sbita, Lassaad

    2017-01-01

    Highlights: • Create a simple, easy of implement and accurate V_M_P_P estimator. • Stability analysis of the proposed system based on the Lyapunov’s theory. • A comparative study versus P&O, highlight SMC good performances. • Construct a new PS-SMC algorithm to include the partial shadow case. • Experimental validation of the SMC MPP tracker. - Abstract: This paper presents a photovoltaic (PV) system with a maximum power point tracking (MPPT) facility. The goal of this work is to maximize power extraction from the photovoltaic generator (PVG). This goal is achieved using a sliding mode controller (SMC) that drives a boost converter connected between the PVG and the load. The system is modeled and tested under MATLAB/SIMULINK environment. In simulation, the sliding mode controller offers fast and accurate convergence to the maximum power operating point that outperforms the well-known perturbation and observation method (P&O). The sliding mode controller performance is evaluated during steady-state, against load varying and panel partial shadow (PS) disturbances. To confirm the above conclusion, a practical implementation of the maximum power point tracker based sliding mode controller on a hardware setup is performed on a dSPACE real time digital control platform. The data acquisition and the control system are conducted all around dSPACE 1104 controller board and its RTI environment. The experimental results demonstrate the validity of the proposed control scheme over a stand-alone real photovoltaic system.

  6. Earthquake likelihood model testing

    Science.gov (United States)

    Schorlemmer, D.; Gerstenberger, M.C.; Wiemer, S.; Jackson, D.D.; Rhoades, D.A.

    2007-01-01

    INTRODUCTIONThe Regional Earthquake Likelihood Models (RELM) project aims to produce and evaluate alternate models of earthquake potential (probability per unit volume, magnitude, and time) for California. Based on differing assumptions, these models are produced to test the validity of their assumptions and to explore which models should be incorporated in seismic hazard and risk evaluation. Tests based on physical and geological criteria are useful but we focus on statistical methods using future earthquake catalog data only. We envision two evaluations: a test of consistency with observed data and a comparison of all pairs of models for relative consistency. Both tests are based on the likelihood method, and both are fully prospective (i.e., the models are not adjusted to fit the test data). To be tested, each model must assign a probability to any possible event within a specified region of space, time, and magnitude. For our tests the models must use a common format: earthquake rates in specified “bins” with location, magnitude, time, and focal mechanism limits.Seismology cannot yet deterministically predict individual earthquakes; however, it should seek the best possible models for forecasting earthquake occurrence. This paper describes the statistical rules of an experiment to examine and test earthquake forecasts. The primary purposes of the tests described below are to evaluate physical models for earthquakes, assure that source models used in seismic hazard and risk studies are consistent with earthquake data, and provide quantitative measures by which models can be assigned weights in a consensus model or be judged as suitable for particular regions.In this paper we develop a statistical method for testing earthquake likelihood models. A companion paper (Schorlemmer and Gerstenberger 2007, this issue) discusses the actual implementation of these tests in the framework of the RELM initiative.Statistical testing of hypotheses is a common task and a

  7. Evaluation of tomographic image quality of extended and conventional parallel hole collimators using maximum likelihood expectation maximization algorithm by Monte Carlo simulations.

    Science.gov (United States)

    Moslemi, Vahid; Ashoor, Mansour

    2017-10-01

    One of the major problems associated with parallel hole collimators (PCs) is the trade-off between their resolution and sensitivity. To solve this problem, a novel PC - namely, extended parallel hole collimator (EPC) - was proposed, in which particular trapezoidal denticles were increased upon septa on the side of the detector. In this study, an EPC was designed and its performance was compared with that of two PCs, PC35 and PC41, with a hole size of 1.5 mm and hole lengths of 35 and 41 mm, respectively. The Monte Carlo method was used to calculate the important parameters such as resolution, sensitivity, scattering, and penetration ratio. A Jaszczak phantom was also simulated to evaluate the resolution and contrast of tomographic images, which were produced by the EPC6, PC35, and PC41 using the Monte Carlo N-particle version 5 code, and tomographic images were reconstructed by using maximum likelihood expectation maximization algorithm. Sensitivity of the EPC6 was increased by 20.3% in comparison with that of the PC41 at the identical spatial resolution and full-width at tenth of maximum here. Moreover, the penetration and scattering ratio of the EPC6 was 1.2% less than that of the PC41. The simulated phantom images show that the EPC6 increases contrast-resolution and contrast-to-noise ratio compared with those of PC41 and PC35. When compared with PC41 and PC35, EPC6 improved trade-off between resolution and sensitivity, reduced penetrating and scattering ratios, and produced images with higher quality. EPC6 can be used to increase detectability of more details in nuclear medicine images.

  8. Searching for degenerate Higgs bosons a profile likelihood ratio method to test for mass-degenerate states in the presence of censored data and uncertainties

    CERN Document Server

    David, André; Petrucciani, Giovanni

    2015-01-01

    Using the likelihood ratio test statistic, we present a method which can be employed to test the hypothesis of a single Higgs boson using the matrix of measured signal strengths. This method can be applied in the presence of censored data and takes into account uncertainties on the measurements. The p-value against the hypothesis of a single Higgs boson is defined from the expected distribution of the test statistic, generated using pseudo-experiments. The applicability of the likelihood-based test is demonstrated using numerical examples with uncertainties and missing matrix elements.

  9. Statistical modelling of survival data with random effects h-likelihood approach

    CERN Document Server

    Ha, Il Do; Lee, Youngjo

    2017-01-01

    This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to research...

  10. Ringing Artefact Reduction By An Efficient Likelihood Improvement Method

    Science.gov (United States)

    Fuderer, Miha

    1989-10-01

    In MR imaging, the extent of the acquired spatial frequencies of the object is necessarily finite. The resulting image shows artefacts caused by "truncation" of its Fourier components. These are known as Gibbs artefacts or ringing artefacts. These artefacts are particularly. visible when the time-saving reduced acquisition method is used, say, when scanning only the lowest 70% of the 256 data lines. Filtering the data results in loss of resolution. A method is described that estimates the high frequency data from the low-frequency data lines, with the likelihood of the image as criterion. It is a computationally very efficient method, since it requires practically only two extra Fourier transforms, in addition to the normal. reconstruction. The results of this method on MR images of human subjects are promising. Evaluations on a 70% acquisition image show about 20% decrease of the error energy after processing. "Error energy" is defined as the total power of the difference to a 256-data-lines reference image. The elimination of ringing artefacts then appears almost complete..

  11. A comparison of ancestral state reconstruction methods for quantitative characters.

    Science.gov (United States)

    Royer-Carenzi, Manuela; Didier, Gilles

    2016-09-07

    Choosing an ancestral state reconstruction method among the alternatives available for quantitative characters may be puzzling. We present here a comparison of seven of them, namely the maximum likelihood, restricted maximum likelihood, generalized least squares under Brownian, Brownian-with-trend and Ornstein-Uhlenbeck models, phylogenetic independent contrasts and squared parsimony methods. A review of the relations between these methods shows that the maximum likelihood, the restricted maximum likelihood and the generalized least squares under Brownian model infer the same ancestral states and can only be distinguished by the distributions accounting for the reconstruction uncertainty which they provide. The respective accuracy of the methods is assessed over character evolution simulated under a Brownian motion with (and without) directional or stabilizing selection. We give the general form of ancestral state distributions conditioned on leaf states under the simulation models. Ancestral distributions are used first, to give a theoretical lower bound of the expected reconstruction error, and second, to develop an original evaluation scheme which is more efficient than comparing the reconstructed and the simulated states. Our simulations show that: (i) the distributions of the reconstruction uncertainty provided by the methods generally make sense (some more than others); (ii) it is essential to detect the presence of an evolutionary trend and to choose a reconstruction method accordingly; (iii) all the methods show good performances on characters under stabilizing selection; (iv) without trend or stabilizing selection, the maximum likelihood method is generally the most accurate. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. The reliability assessment of the electromagnetic valve of high-speed electric multiple units braking system based on two-parameter exponential distribution

    Directory of Open Access Journals (Sweden)

    Jianwei Yang

    2016-06-01

    Full Text Available In order to solve the reliability assessment of braking system component of high-speed electric multiple units, this article, based on two-parameter exponential distribution, provides the maximum likelihood estimation and Bayes estimation under a type-I life test. First of all, we evaluate the failure probability value according to the classical estimation method and then obtain the maximum likelihood estimation of parameters of two-parameter exponential distribution by performing and using the modified likelihood function. On the other hand, based on Bayesian theory, this article also selects the beta and gamma distributions as the prior distribution, combines with the modified maximum likelihood function, and innovatively applies a Markov chain Monte Carlo algorithm to parameters assessment based on Bayes estimation method for two-parameter exponential distribution, so that two reliability mathematical models of the electromagnetic valve are obtained. Finally, through type-I life test, the failure rates according to maximum likelihood estimation and Bayes estimation method based on Markov chain Monte Carlo algorithm are, respectively, 2.650 × 10−5 and 3.037 × 10−5. Compared with the failure rate of a electromagnetic valve 3.005 × 10−5, it proves that the Bayes method can use a Markov chain Monte Carlo algorithm to estimate reliability for two-parameter exponential distribution and Bayes estimation is more closer to the value of electromagnetic valve. So, by fully integrating multi-source, Bayes estimation method can preferably modify and precisely estimate the parameters, which can provide a certain theoretical basis for the safety operation of high-speed electric multiple units.

  13. Likelihood-Based Inference in Nonlinear Error-Correction Models

    DEFF Research Database (Denmark)

    Kristensen, Dennis; Rahbæk, Anders

    We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relation- ships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long- run cointegration parameters, and the short-run parameters. Asymp- totic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study...

  14. A maximum likelihood approach to generate hypotheses on the evolution and historical biogeography in the Lower Volga Valley regions (southwest Russia)

    Science.gov (United States)

    Mavrodiev, Evgeny V; Laktionov, Alexy P; Cellinese, Nico

    2012-01-01

    The evolution of the diverse flora in the Lower Volga Valley (LVV) (southwest Russia) is complex due to the composite geomorphology and tectonic history of the Caspian Sea and adjacent areas. In the absence of phylogenetic studies and temporal information, we implemented a maximum likelihood (ML) approach and stochastic character mapping reconstruction aiming at recovering historical signals from species occurrence data. A taxon-area matrix of 13 floristic areas and 1018 extant species was constructed and analyzed with RAxML and Mesquite. Additionally, we simulated scenarios with numbers of hypothetical extinct taxa from an unknown palaeoflora that occupied the areas before the dramatic transgression and regression events that have occurred from the Pleistocene to the present day. The flora occurring strictly along the river valley and delta appear to be younger than that of adjacent steppes and desert-like regions, regardless of the chronology of transgression and regression events that led to the geomorphological formation of the LVV. This result is also supported when hypothetical extinct taxa are included in the analyses. The history of each species was inferred by using a stochastic character mapping reconstruction method as implemented in Mesquite. Individual histories appear to be independent from one another and have been shaped by repeated dispersal and extinction events. These reconstructions provide testable hypotheses for more in-depth investigations of their population structure and dynamics. PMID:22957179

  15. Maximum mutual information vector quantization of log-likelihood ratios for memory efficient HARQ implementations

    DEFF Research Database (Denmark)

    Danieli, Matteo; Forchhammer, Søren; Andersen, Jakob Dahl

    2010-01-01

    analysis leads to using maximum mutual information (MMI) as optimality criterion and in turn Kullback-Leibler (KL) divergence as distortion measure. Simulations run based on an LTE-like system have proven that VQ can be implemented in a computationally simple way at low rates of 2-3 bits per LLR value......Modern mobile telecommunication systems, such as 3GPP LTE, make use of Hybrid Automatic Repeat reQuest (HARQ) for efficient and reliable communication between base stations and mobile terminals. To this purpose, marginal posterior probabilities of the received bits are stored in the form of log...

  16. Robust Methods for Moderation Analysis with a Two-Level Regression Model.

    Science.gov (United States)

    Yang, Miao; Yuan, Ke-Hai

    2016-01-01

    Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

  17. 230Th and 234Th as coupled tracers of particle cycling in the ocean: A maximum likelihood approach

    Science.gov (United States)

    Wang, Wei-Lei; Armstrong, Robert A.; Cochran, J. Kirk; Heilbrun, Christina

    2016-05-01

    We applied maximum likelihood estimation to measurements of Th isotopes (234,230Th) in Mediterranean Sea sediment traps that separated particles according to settling velocity. This study contains two unique aspects. First, it relies on settling velocities that were measured using sediment traps, rather than on measured particle sizes and an assumed relationship between particle size and sinking velocity. Second, because of the labor and expense involved in obtaining these data, they were obtained at only a few depths, and their analysis required constructing a new type of box-like model, which we refer to as a "two-layer" model, that we then analyzed using likelihood techniques. Likelihood techniques were developed in the 1930s by statisticians, and form the computational core of both Bayesian and non-Bayesian statistics. Their use has recently become very popular in ecology, but they are relatively unknown in geochemistry. Our model was formulated by assuming steady state and first-order reaction kinetics for thorium adsorption and desorption, and for particle aggregation, disaggregation, and remineralization. We adopted a cutoff settling velocity (49 m/d) from Armstrong et al. (2009) to separate particles into fast- and slow-sinking classes. A unique set of parameters with no dependence on prior values was obtained. Adsorption rate constants for both slow- and fast-sinking particles are slightly higher in the upper layer than in the lower layer. Slow-sinking particles have higher adsorption rate constants than fast-sinking particles. Desorption rate constants are higher in the lower layer (slow-sinking particles: 13.17 ± 1.61, fast-sinking particles: 13.96 ± 0.48) than in the upper layer (slow-sinking particles: 7.87 ± 0.60 y-1, fast-sinking particles: 1.81 ± 0.44 y-1). Aggregation rate constants were higher, 1.88 ± 0.04, in the upper layer and just 0.07 ± 0.01 y-1 in the lower layer. Disaggregation rate constants were just 0.30 ± 0.10 y-1 in the upper

  18. Maximum a posteriori decoder for digital communications

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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 ...

  20. Inference for the Sharpe Ratio Using a Likelihood-Based Approach

    Directory of Open Access Journals (Sweden)

    Ying Liu

    2012-01-01

    Full Text Available The Sharpe ratio is the prominent risk-adjusted performance measure used by practitioners. Statistical testing of this ratio using its asymptotic distribution has lagged behind its use. In this paper, highly accurate likelihood analysis is applied for inference on the Sharpe ratio. Both the one- and two-sample problems are considered. The methodology has O(n−3/2 distributional accuracy and can be implemented using any parametric return distribution structure. Simulations are provided to demonstrate the method's superior accuracy over existing methods used for testing in the literature.

  1. Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

    Science.gov (United States)

    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

  2. H.264 SVC Complexity Reduction Based on Likelihood Mode Decision.

    Science.gov (United States)

    Balaji, L; Thyagharajan, K K

    2015-01-01

    H.264 Advanced Video Coding (AVC) was prolonged to Scalable Video Coding (SVC). SVC executes in different electronics gadgets such as personal computer, HDTV, SDTV, IPTV, and full-HDTV in which user demands various scaling of the same content. The various scaling is resolution, frame rate, quality, heterogeneous networks, bandwidth, and so forth. Scaling consumes more encoding time and computational complexity during mode selection. In this paper, to reduce encoding time and computational complexity, a fast mode decision algorithm based on likelihood mode decision (LMD) is proposed. LMD is evaluated in both temporal and spatial scaling. From the results, we conclude that LMD performs well, when compared to the previous fast mode decision algorithms. The comparison parameters are time, PSNR, and bit rate. LMD achieve time saving of 66.65% with 0.05% detriment in PSNR and 0.17% increment in bit rate compared with the full search method.

  3. The maximum-entropy method in superspace

    Czech Academy of Sciences Publication Activity Database

    van Smaalen, S.; Palatinus, Lukáš; Schneider, M.

    2003-01-01

    Roč. 59, - (2003), s. 459-469 ISSN 0108-7673 Grant - others:DFG(DE) XX Institutional research plan: CEZ:AV0Z1010914 Keywords : maximum-entropy method, * aperiodic crystals * electron density Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 1.558, year: 2003

  4. Essays on empirical likelihood in economics

    NARCIS (Netherlands)

    Gao, Z.

    2012-01-01

    This thesis intends to exploit the roots of empirical likelihood and its related methods in mathematical programming and computation. The roots will be connected and the connections will induce new solutions for the problems of estimation, computation, and generalization of empirical likelihood.

  5. Development and Performance of Detectors for the Cryogenic Dark Matter Search Experiment with an Increased Sensitivity Based on a Maximum Likelihood Analysis of Beta Contamination

    Energy Technology Data Exchange (ETDEWEB)

    Driscoll, Donald D [Case Western Reserve Univ., Cleveland, OH (United States)

    2004-05-01

    of a beta-eliminating cut based on a maximum-likelihood characterization described above.

  6. Validation of Likelihood Ratio Methods Used for Forensic Evidence Evaluation: Application in Forensic Fingerprints

    NARCIS (Netherlands)

    Haraksim, Rudolf

    2014-01-01

    In this chapter the Likelihood Ratio (LR) inference model will be introduced, the theoretical aspects of probabilities will be discussed and the validation framework for LR methods used for forensic evidence evaluation will be presented. Prior to introducing the validation framework, following

  7. Obtaining reliable Likelihood Ratio tests from simulated likelihood functions

    DEFF Research Database (Denmark)

    Andersen, Laura Mørch

    It is standard practice by researchers and the default option in many statistical programs to base test statistics for mixed models on simulations using asymmetric draws (e.g. Halton draws). This paper shows that when the estimated likelihood functions depend on standard deviations of mixed param...

  8. A scaling transformation for classifier output based on likelihood ratio: Applications to a CAD workstation for diagnosis of breast cancer

    International Nuclear Information System (INIS)

    Horsch, Karla; Pesce, Lorenzo L.; Giger, Maryellen L.; Metz, Charles E.; Jiang Yulei

    2012-01-01

    Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the ''scale'' of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. Methods: Using both simulated and radiologists' rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on ''matching'' classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist's ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. Results: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. Conclusions: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic.

  9. On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

    Science.gov (United States)

    Karabatsos, George

    2018-06-01

    This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

  10. A Trustworthiness Evaluation Method for Software Architectures Based on the Principle of Maximum Entropy (POME and the Grey Decision-Making Method (GDMM

    Directory of Open Access Journals (Sweden)

    Rong Jiang

    2014-09-01

    Full Text Available As the early design decision-making structure, a software architecture plays a key role in the final software product quality and the whole project. In the software design and development process, an effective evaluation of the trustworthiness of a software architecture can help making scientific and reasonable decisions on the architecture, which are necessary for the construction of highly trustworthy software. In consideration of lacking the trustworthiness evaluation and measurement studies for software architecture, this paper provides one trustworthy attribute model of software architecture. Based on this model, the paper proposes to use the Principle of Maximum Entropy (POME and Grey Decision-making Method (GDMM as the trustworthiness evaluation method of a software architecture and proves the scientificity and rationality of this method, as well as verifies the feasibility through case analysis.

  11. Discrimination method of large log-likelihood study in differential diagnosis of pulmonary diffuse mild micro-nodule

    International Nuclear Information System (INIS)

    Chen Budong; Ma Daqing; He Wen; Tang Hongqu; Qian Linxue; Zhou Ronglin

    2001-01-01

    Objective: To analyze HRCT and thin-slice CT scan findings in 150 patients with pulmonary diffuse mild micro-nodule, and to find the features with the purpose of identifying random micro-nodule, peri-lymphatic micro-nodule, and centrilobular micro-nodule. Methods: The useful features i 150 patients with pulmonary diffuse mild micro-nodule were translated into scores by means of discrimination method of large log-likelihood to identify the micro-nodular category. Results: The accuracy of diagnosis was 94.0% for random micro-nodule, 76.0% for peri-lymphatic micro-nodule, and 90.0% for centrilobular micro-nodule. Conclusion: HRCT and thin-slice CT scans were helpful in differential diagnosis of pulmonary diffuse mild micro-nodule. The discrimination method of large log-likelihood was propitious to diagnosis and differential diagnosis

  12. H.264 SVC Complexity Reduction Based on Likelihood Mode Decision

    Directory of Open Access Journals (Sweden)

    L. Balaji

    2015-01-01

    Full Text Available H.264 Advanced Video Coding (AVC was prolonged to Scalable Video Coding (SVC. SVC executes in different electronics gadgets such as personal computer, HDTV, SDTV, IPTV, and full-HDTV in which user demands various scaling of the same content. The various scaling is resolution, frame rate, quality, heterogeneous networks, bandwidth, and so forth. Scaling consumes more encoding time and computational complexity during mode selection. In this paper, to reduce encoding time and computational complexity, a fast mode decision algorithm based on likelihood mode decision (LMD is proposed. LMD is evaluated in both temporal and spatial scaling. From the results, we conclude that LMD performs well, when compared to the previous fast mode decision algorithms. The comparison parameters are time, PSNR, and bit rate. LMD achieve time saving of 66.65% with 0.05% detriment in PSNR and 0.17% increment in bit rate compared with the full search method.

  13. Maximum Power Point Tracking Control of Photovoltaic Systems: A Polynomial Fuzzy Model-Based Approach

    DEFF Research Database (Denmark)

    Rakhshan, Mohsen; Vafamand, Navid; Khooban, Mohammad Hassan

    2018-01-01

    This paper introduces a polynomial fuzzy model (PFM)-based maximum power point tracking (MPPT) control approach to increase the performance and efficiency of the solar photovoltaic (PV) electricity generation. The proposed method relies on a polynomial fuzzy modeling, a polynomial parallel......, a direct maximum power (DMP)-based control structure is considered for MPPT. Using the PFM representation, the DMP-based control structure is formulated in terms of SOS conditions. Unlike the conventional approaches, the proposed approach does not require exploring the maximum power operational point...

  14. Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

    Directory of Open Access Journals (Sweden)

    Daniel Ramos

    2017-02-01

    Full Text Available Data to which the authors refer to throughout this article are likelihood ratios (LR computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR method in forensic evidence evaluation. These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim, [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1]. Alongside the data, a justification and motivation for the use of methods is given. These methods calculate LRs from the fingerprint/mark data and are subject to a validation procedure. The choice of using real forensic fingerprint in the validation and simulated data in the development is described and justified. Validation criteria are set for the purpose of validation of the LR methods, which are used to calculate the LR values from the data and the validation report. For privacy and data protection reasons, the original fingerprint/mark images cannot be shared. But these images do not constitute the core data for the validation, contrarily to the LRs that are shared.

  15. Maximum likelihood phylogenetic reconstruction from high-resolution whole-genome data and a tree of 68 eukaryotes.

    Science.gov (United States)

    Lin, Yu; Hu, Fei; Tang, Jijun; Moret, Bernard M E

    2013-01-01

    The rapid accumulation of whole-genome data has renewed interest in the study of the evolution of genomic architecture, under such events as rearrangements, duplications, losses. Comparative genomics, evolutionary biology, and cancer research all require tools to elucidate the mechanisms, history, and consequences of those evolutionary events, while phylogenetics could use whole-genome data to enhance its picture of the Tree of Life. Current approaches in the area of phylogenetic analysis are limited to very small collections of closely related genomes using low-resolution data (typically a few hundred syntenic blocks); moreover, these approaches typically do not include duplication and loss events. We describe a maximum likelihood (ML) approach for phylogenetic analysis that takes into account genome rearrangements as well as duplications, insertions, and losses. Our approach can handle high-resolution genomes (with 40,000 or more markers) and can use in the same analysis genomes with very different numbers of markers. Because our approach uses a standard ML reconstruction program (RAxML), it scales up to large trees. We present the results of extensive testing on both simulated and real data showing that our approach returns very accurate results very quickly. In particular, we analyze a dataset of 68 high-resolution eukaryotic genomes, with from 3,000 to 42,000 genes, from the eGOB database; the analysis, including bootstrapping, takes just 3 hours on a desktop system and returns a tree in agreement with all well supported branches, while also suggesting resolutions for some disputed placements.

  16. Theoretical Study of Penalized-Likelihood Image Reconstruction for Region of Interest Quantification

    International Nuclear Information System (INIS)

    Qi, Jinyi; Huesman, Ronald H.

    2006-01-01

    Region of interest (ROI) quantification is an important task in emission tomography (e.g., positron emission tomography and single photon emission computed tomography). It is essential for exploring clinical factors such as tumor activity, growth rate, and the efficacy of therapeutic interventions. Statistical image reconstruction methods based on the penalized maximum-likelihood (PML) or maximum a posteriori principle have been developed for emission tomography to deal with the low signal-to-noise ratio of the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the regularization parameter in PML reconstruction controls the resolution and noise tradeoff and, hence, affects ROI quantification. In this paper, we theoretically analyze the performance of ROI quantification in PML reconstructions. Building on previous work, we derive simplified theoretical expressions for the bias, variance, and ensemble mean-squared-error (EMSE) of the estimated total activity in an ROI that is surrounded by a uniform background. When the mean and covariance matrix of the activity inside the ROI are known, the theoretical expressions are readily computable and allow for fast evaluation of image quality for ROI quantification with different regularization parameters. The optimum regularization parameter can then be selected to minimize the EMSE. Computer simulations are conducted for small ROIs with variable uniform uptake. The results show that the theoretical predictions match the Monte Carlo results reasonably well

  17. Likelihood-based inference for cointegration with nonlinear error-correction

    DEFF Research Database (Denmark)

    Kristensen, Dennis; Rahbek, Anders Christian

    2010-01-01

    We consider a class of nonlinear vector error correction models where the transfer function (or loadings) of the stationary relationships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long-run cointegration parameters, and the short-run parameters. Asymptotic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normality can be found. A simulation study...

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

    Science.gov (United States)

    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

  19. Assessing the accuracy of ancestral protein reconstruction methods.

    Directory of Open Access Journals (Sweden)

    Paul D Williams

    2006-06-01

    Full Text Available The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of "ancestral sequences" inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a "best guess" amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.

  20. Assessing the accuracy of ancestral protein reconstruction methods.

    Science.gov (United States)

    Williams, Paul D; Pollock, David D; Blackburne, Benjamin P; Goldstein, Richard A

    2006-06-23

    The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of "ancestral sequences" inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a "best guess" amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.

  1. LIKELIHOOD ESTIMATION OF PARAMETERS USING SIMULTANEOUSLY MONITORED PROCESSES

    DEFF Research Database (Denmark)

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2004-01-01

    The topic is maximum likelihood inference from several simultaneously monitored response processes of a structure to obtain knowledge about the parameters of other not monitored but important response processes when the structure is subject to some Gaussian load field in space and time. The consi....... The considered example is a ship sailing with a given speed through a Gaussian wave field....

  2. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.

    Science.gov (United States)

    Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J

    2017-03-03

    We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

  3. Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981-2015

    Science.gov (United States)

    Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

    2018-04-01

    The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.

  4. Tip Speed Ratio Based Maximum Power Tracking Control of Variable Speed Wind Turbines; A Comprehensive Design

    Directory of Open Access Journals (Sweden)

    Murat Karabacak

    2017-08-01

    Full Text Available The most primitive control method of wind turbines used to generate electric energy from wind is the fixed speed control method. With this method, it is not possible that turbine input power is transferred to grid at maximum rate. For this reason, Maximum Power Tracking (MPT schemes are proposed. In order to implement MPT, the propeller has to rotate at a different speed for every different wind speed. This situation has led MPT based systems to be called Variable Speed Wind Turbine (VSWT systems. In VSWT systems, turbine input power can be transferred to grid at rates close to maximum power. When MPT based control of VSWT systems is the case, two important processes come into prominence. These are instantaneously determination and tracking of MPT point. In this study, using a Maximum Power Point Tracking (MPPT method based on tip speed ratio, power available in wind is transferred into grid over a back to back converter at maximum rate via a VSWT system with permanent magnet synchronous generator (PMSG. Besides a physical wind turbine simulator is modelled and simulated. Results show that a time varying MPPT point is tracked with a high performance.

  5. Effect of a High-intensity Interval Training method on maximum oxygen consumption in Chilean schoolchildren

    Directory of Open Access Journals (Sweden)

    Sergio Galdames-Maliqueo

    2017-12-01

    Full Text Available Introduction: The low levels of maximum oxygen consumption (VO2max evaluated in Chilean schoolchildren suggest the startup of trainings that improve the aerobic capacity. Objective: To analyze the effect of a High-intensity Interval Training method on maximum oxygen consumption in Chilean schoolchildren. Materials and methods: Thirty-two high school students from the eighth grade, who were divided into two groups, were part of the study (experimental group = 16 students and control group = 16 students. The main analyzed variable was the maximum oxygen consumption through the Course Navette Test. A High-intensity Interval training method was applied based on the maximum aerobic speed obtained through the Test. A mixed ANOVA was used for statistical analysis. Results: The experimental group showed a significant increase in the Maximum Oxygen Consumption between the pretest and posttest when compared with the control group (p < 0.0001. Conclusion: The results of the study showed a positive effect of the High-intensity Interval Training on the maximum consumption of oxygen. At the end of the study, it is concluded that High-intensity Interval Training is a good stimulation methodology for Chilean schoolchildren.

  6. A Reliability Test of a Complex System Based on Empirical Likelihood

    OpenAIRE

    Zhou, Yan; Fu, Liya; Zhang, Jun; Hui, Yongchang

    2016-01-01

    To analyze the reliability of a complex system described by minimal paths, an empirical likelihood method is proposed to solve the reliability test problem when the subsystem distributions are unknown. Furthermore, we provide a reliability test statistic of the complex system and extract the limit distribution of the test statistic. Therefore, we can obtain the confidence interval for reliability and make statistical inferences. The simulation studies also demonstrate the theorem results.

  7. Influence of Neutron Spectra Unfolding Method on Fast Neutron Dose Determination

    International Nuclear Information System (INIS)

    Marinkovic, P.

    1991-01-01

    Full text: Accuracy of knowing the fast neutron spectra has great influence on equivalent dose determination. In usual fast neutron spectrum measurements with scintillation detectors based on proton recoil, the main difficulty is confidence of unfolding method. In former ones variance of obtained result is usually great and negative values are possible too, which does means that we don't now exactly is obtained neutron spectrum real one. The new unfolding method based on Shanon's information theory, which gives non-negative spectrum and relative low variance, is obtained and appropriate numerical code for application in fast neutron spectrometry based on proton recoil is realized. In this method principle of maximum entropy and maximum likelihood are used together. Unknown group density distribution functions, which are considered as desired normalized mean neutron group flux, are constl u cted using only constrain of knowing mean value. Obtained distributions are consistent to available information (counts in NCA from proton recoil), while being maximally noncommittal with respect to all other unknown circumstances. For maximum likelihood principle, distribution functions around mean value of counts in the channels of MCA are taken to be Gauss function shape. Optimal non-negative solution is searched by means of Lagrange parameter method. Nonlinear system of equations, is solved using gradient and Newton iterative algorithm. Error covariance matrix is obtained too. (author)

  8. Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

    Science.gov (United States)

    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.

  9. Point Estimation Method of Electromagnetic Flowmeters Life Based on Randomly Censored Failure Data

    Directory of Open Access Journals (Sweden)

    Zhen Zhou

    2014-08-01

    Full Text Available This paper analyzes the characteristics of the enterprise after-sale service records for field failure data, and summarizes the types of field data. Maximum likelihood estimation method and the least squares method are presented for the complexity and difficulty of field failure data processing, and Monte Carlo simulation method is proposed. Monte Carlo simulation, the relatively simple calculation method, is an effective method, whose result is closed to that of the other two methods. Through the after-sale service records analysis of a specific electromagnetic flowmeter enterprises, this paper illustrates the effectiveness of field failure data processing methods.

  10. Maximum entropy and Bayesian methods

    International Nuclear Information System (INIS)

    Smith, C.R.; Erickson, G.J.; Neudorfer, P.O.

    1992-01-01

    Bayesian probability theory and Maximum Entropy methods are at the core of a new view of scientific inference. These 'new' ideas, along with the revolution in computational methods afforded by modern computers allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics. The title workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this book. There are tutorial and theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. Contributions contained in this volume present a state-of-the-art overview that will be influential and useful for many years to come

  11. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

    Science.gov (United States)

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  12. A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN)

    Energy Technology Data Exchange (ETDEWEB)

    Karlis, A.D. [Electrical Machines Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece); Kottas, T.L.; Boutalis, Y.S. [Automatic Control Systems Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece)

    2007-03-15

    Maximum power point trackers (MPPTs) play an important role in photovoltaic (PV) power systems because they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. This paper presents a novel MPPT method based on fuzzy cognitive networks (FCN). The new method gives a good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The numerical results show the effectiveness of the proposed algorithm. (author)

  13. A maximum power point tracking for photovoltaic-SPE system using a maximum current controller

    Energy Technology Data Exchange (ETDEWEB)

    Muhida, Riza [Osaka Univ., Dept. of Physical Science, Toyonaka, Osaka (Japan); Osaka Univ., Dept. of Electrical Engineering, Suita, Osaka (Japan); Park, Minwon; Dakkak, Mohammed; Matsuura, Kenji [Osaka Univ., Dept. of Electrical Engineering, Suita, Osaka (Japan); Tsuyoshi, Akira; Michira, Masakazu [Kobe City College of Technology, Nishi-ku, Kobe (Japan)

    2003-02-01

    Processes to produce hydrogen from solar photovoltaic (PV)-powered water electrolysis using solid polymer electrolysis (SPE) are reported. An alternative control of maximum power point tracking (MPPT) in the PV-SPE system based on the maximum current searching methods has been designed and implemented. Based on the characteristics of voltage-current and theoretical analysis of SPE, it can be shown that the tracking of the maximum current output of DC-DC converter in SPE side will track the MPPT of photovoltaic panel simultaneously. This method uses a proportional integrator controller to control the duty factor of DC-DC converter with pulse-width modulator (PWM). The MPPT performance and hydrogen production performance of this method have been evaluated and discussed based on the results of the experiment. (Author)

  14. A Maximum Entropy Method for a Robust Portfolio Problem

    Directory of Open Access Journals (Sweden)

    Yingying Xu

    2014-06-01

    Full Text Available We propose a continuous maximum entropy method to investigate the robustoptimal portfolio selection problem for the market with transaction costs and dividends.This robust model aims to maximize the worst-case portfolio return in the case that allof asset returns lie within some prescribed intervals. A numerical optimal solution tothe problem is obtained by using a continuous maximum entropy method. Furthermore,some numerical experiments indicate that the robust model in this paper can result in betterportfolio performance than a classical mean-variance model.

  15. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

    Directory of Open Access Journals (Sweden)

    Anthony Hoak

    2017-03-01

    Full Text Available We develop an interactive likelihood (ILH for sequential Monte Carlo (SMC methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL and TUD-Stadtmitte using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA and classification of events, activities and relationships for multi-object trackers (CLEAR MOT. In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

  16. The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2004-01-01

    Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.

  17. Performance Analysis of a Maximum Power Point Tracking Technique using Silver Mean Method

    Directory of Open Access Journals (Sweden)

    Shobha Rani Depuru

    2018-01-01

    Full Text Available The proposed paper presents a simple and particularly efficacious Maximum Power Point Tracking (MPPT algorithm based on Silver Mean Method (SMM. This method operates by choosing a search interval from the P-V characteristics of the given solar array and converges to MPP of the Solar Photo-Voltaic (SPV system by shrinking its interval. After achieving the maximum power, the algorithm stops shrinking and maintains constant voltage until the next interval is decided. The tracking capability efficiency and performance analysis of the proposed algorithm are validated by the simulation and experimental results with a 100W solar panel for variable temperature and irradiance conditions. The results obtained confirm that even without any perturbation and observation process, the proposed method still outperforms the traditional perturb and observe (P&O method by demonstrating far better steady state output, more accuracy and higher efficiency.

  18. Corporate brand extensions based on the purchase likelihood: governance implications

    Directory of Open Access Journals (Sweden)

    Spyridon Goumas

    2018-03-01

    Full Text Available This paper is examining the purchase likelihood of hypothetical service brand extensions from product companies focusing on consumer electronics based on sector categorization and perceptions of fit between the existing product category and image of the company. Prior research has recognized that levels of brand knowledge eases the transference of associations and affect to the new products. Similarity to the existing products of the parent company and perceived image also influence the success of brand extensions. However, sector categorization may interfere with this relationship. The purpose of this study is to examine Greek consumers’ attitudes towards hypothetical brand extensions, and how these are affected by consumers’ existing knowledge about the brand, sector categorization and perceptions of image and category fit of cross-sector extensions. This aim is examined in the context of technological categories, where less-known companies exhibited significance in purchase likelihood, and contradictory with the existing literature, service companies did not perform as positively as expected. Additional insights to the existing literature about sector categorization are provided. The effect of both image and category fit is also examined and predictions regarding the effect of each are made.

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

    Directory of Open Access Journals (Sweden)

    Milinkovitch Michel C

    2010-07-01

    Full Text Available Abstract 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

  20. Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Ammar Al-Gizi

    2017-07-01

    Full Text Available This paper examines the efficiency of a fuzzy logic control (FLC based maximum power point tracking (MPPT of a photovoltaic (PV system under variable climate conditions and connected load requirements. The PV system including a PV module BP SX150S, buck-boost DC-DC converter, MPPT, and a resistive load is modeled and simulated using Matlab/Simulink package. In order to compare the performance of FLC-based MPPT controller with the conventional perturb and observe (P&O method at different irradiation (G, temperature (T and connected load (RL variations – rising time (tr, recovering time, total average power and MPPT efficiency topics are calculated. The simulation results show that the FLC-based MPPT method can quickly track the maximum power point (MPP of the PV module at the transient state and effectively eliminates the power oscillation around the MPP of the PV module at steady state, hence more average power can be extracted, in comparison with the conventional P&O method.

  1. Likelihood functions for the analysis of single-molecule binned photon sequences

    Energy Technology Data Exchange (ETDEWEB)

    Gopich, Irina V., E-mail: irinag@niddk.nih.gov [Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892 (United States)

    2012-03-02

    Graphical abstract: Folding of a protein with attached fluorescent dyes, the underlying conformational trajectory of interest, and the observed binned photon trajectory. Highlights: Black-Right-Pointing-Pointer A sequence of photon counts can be analyzed using a likelihood function. Black-Right-Pointing-Pointer The exact likelihood function for a two-state kinetic model is provided. Black-Right-Pointing-Pointer Several approximations are considered for an arbitrary kinetic model. Black-Right-Pointing-Pointer Improved likelihood functions are obtained to treat sequences of FRET efficiencies. - Abstract: We consider the analysis of a class of experiments in which the number of photons in consecutive time intervals is recorded. Sequence of photon counts or, alternatively, of FRET efficiencies can be studied using likelihood-based methods. For a kinetic model of the conformational dynamics and state-dependent Poisson photon statistics, the formalism to calculate the exact likelihood that this model describes such sequences of photons or FRET efficiencies is developed. Explicit analytic expressions for the likelihood function for a two-state kinetic model are provided. The important special case when conformational dynamics are so slow that at most a single transition occurs in a time bin is considered. By making a series of approximations, we eventually recover the likelihood function used in hidden Markov models. In this way, not only is insight gained into the range of validity of this procedure, but also an improved likelihood function can be obtained.

  2. A Hybrid Positioning Method Based on Hypothesis Testing

    DEFF Research Database (Denmark)

    Amiot, Nicolas; Pedersen, Troels; Laaraiedh, Mohamed

    2012-01-01

    maxima. We propose to first estimate the support region of the two peaks of the likelihood function using a set membership method, and then decide between the two regions using a rule based on the less reliable observations. Monte Carlo simulations show that the performance of the proposed method...

  3. Likelihood based inference for partially observed renewal processes

    NARCIS (Netherlands)

    van Lieshout, Maria Nicolette Margaretha

    2016-01-01

    This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum

  4. Clarification of the use of chi-square and likelihood functions in fits to histograms

    International Nuclear Information System (INIS)

    Baker, S.; Cousins, R.D.

    1984-01-01

    We consider the problem of fitting curves to histograms in which the data obey multinomial or Poisson statistics. Techniques commonly used by physicists are examined in light of standard results found in the statistics literature. We review the relationship between multinomial and Poisson distributions, and clarify a sufficient condition for equality of the area under the fitted curve and the number of events on the histogram. Following the statisticians, we use the likelihood ratio test to construct a general Z 2 statistic, Zsub(lambda) 2 , which yields parameter and error estimates identical to those of the method of maximum likelihood. The Zsub(lambda) 2 statistic is further useful for testing goodness-of-fit since the value of its minimum asymptotically obeys a classical chi-square distribution. One should be aware, however, of the potential for statistical bias, especially when the number of events is small. (orig.)

  5. Audio-visual Classification and Fusion of Spontaneous Affect Data in Likelihood Space

    NARCIS (Netherlands)

    Nicolaou, Mihalis A.; Gunes, Hatice; Pantic, Maja

    2010-01-01

    This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is

  6. Maximum Likelihood Learning of Conditional MTE Distributions

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2009-01-01

    We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE specifications and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables....... Finally, experimental results demonstrate the applicability of the learning procedure as well as the expressive power of the conditional MTE distribution....

  7. Bayesian risk-based decision method for model validation under uncertainty

    International Nuclear Information System (INIS)

    Jiang Xiaomo; Mahadevan, Sankaran

    2007-01-01

    This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment

  8. Current opinion about maximum entropy methods in Moessbauer spectroscopy

    International Nuclear Information System (INIS)

    Szymanski, K

    2009-01-01

    Current opinion about Maximum Entropy Methods in Moessbauer Spectroscopy is presented. The most important advantage offered by the method is the correct data processing under circumstances of incomplete information. Disadvantage is the sophisticated algorithm and its application to the specific problems.

  9. Approximate Likelihood

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusion for searches as well as mass, cross-section, and coupling measurements. The use of Machine Learning (multivariate) algorithms in HEP is mainly restricted to searches, which can be reduced to classification between two fixed distributions: signal vs. background. I will show how we can extend the use of ML classifiers to distributions parameterized by physical quantities like masses and couplings as well as nuisance parameters associated to systematic uncertainties. This allows for one to approximate the likelihood ratio while still using a high dimensional feature vector for the data. Both the MEM and ABC approaches mentioned above aim to provide inference on model parameters (like cross-sections, masses, couplings, etc.). ABC is fundamentally tied Bayesian inference and focuses on the “likelihood free” setting where only a simulator is available and one cannot directly compute the likelihood for the dat...

  10. Accelerated gradient methods for constrained image deblurring

    International Nuclear Information System (INIS)

    Bonettini, S; Zanella, R; Zanni, L; Bertero, M

    2008-01-01

    In this paper we propose a special gradient projection method for the image deblurring problem, in the framework of the maximum likelihood approach. We present the method in a very general form and we give convergence results under standard assumptions. Then we consider the deblurring problem and the generality of the proposed algorithm allows us to add a energy conservation constraint to the maximum likelihood problem. In order to improve the convergence rate, we devise appropriate scaling strategies and steplength updating rules, especially designed for this application. The effectiveness of the method is evaluated by means of a computational study on astronomical images corrupted by Poisson noise. Comparisons with standard methods for image restoration, such as the expectation maximization algorithm, are also reported.

  11. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  12. Marginal likelihood estimation of negative binomial parameters with applications to RNA-seq data.

    Science.gov (United States)

    León-Novelo, Luis; Fuentes, Claudio; Emerson, Sarah

    2017-10-01

    RNA-Seq data characteristically exhibits large variances, which need to be appropriately accounted for in any proposed model. We first explore the effects of this variability on the maximum likelihood estimator (MLE) of the dispersion parameter of the negative binomial distribution, and propose instead to use an estimator obtained via maximization of the marginal likelihood in a conjugate Bayesian framework. We show, via simulation studies, that the marginal MLE can better control this variation and produce a more stable and reliable estimator. We then formulate a conjugate Bayesian hierarchical model, and use this new estimator to propose a Bayesian hypothesis test to detect differentially expressed genes in RNA-Seq data. We use numerical studies to show that our much simpler approach is competitive with other negative binomial based procedures, and we use a real data set to illustrate the implementation and flexibility of the procedure. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

    Energy Technology Data Exchange (ETDEWEB)

    Raghunathan, Srinivasan; Patil, Sanjaykumar; Bianchini, Federico; Reichardt, Christian L. [School of Physics, University of Melbourne, 313 David Caro building, Swanston St and Tin Alley, Parkville VIC 3010 (Australia); Baxter, Eric J. [Department of Physics and Astronomy, University of Pennsylvania, 209 S. 33rd Street, Philadelphia, PA 19104 (United States); Bleem, Lindsey E. [Argonne National Laboratory, High-Energy Physics Division, 9700 S. Cass Avenue, Argonne, IL 60439 (United States); Crawford, Thomas M. [Kavli Institute for Cosmological Physics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 (United States); Holder, Gilbert P. [Department of Astronomy and Department of Physics, University of Illinois, 1002 West Green St., Urbana, IL 61801 (United States); Manzotti, Alessandro, E-mail: srinivasan.raghunathan@unimelb.edu.au, E-mail: s.patil2@student.unimelb.edu.au, E-mail: ebax@sas.upenn.edu, E-mail: federico.bianchini@unimelb.edu.au, E-mail: bleeml@uchicago.edu, E-mail: tcrawfor@kicp.uchicago.edu, E-mail: gholder@illinois.edu, E-mail: manzotti@uchicago.edu, E-mail: christian.reichardt@unimelb.edu.au [Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 (United States)

    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.

  14. Searching for degenerate Higgs bosons using a profile likelihood ratio method

    CERN Document Server

    Heikkilä, Jaana

    ATLAS and CMS collaborations at the Large Hadron Collider have observed a new resonance con- sistent with the standard model Higgs boson. However, it has been suggested that the observed signal could also be produced by multiple nearly mass-degenerate states that couple differently to the standard model particles. In this work, a method to discriminate between the hypothesis of a single Higgs boson and that of multiple mass-degenerate Higgs bosons was developed. Using the matrix of measured signal strengths in different production and decay modes, parametrizations for the two hypotheses were constructed as a general rank 1 matrix and the most general $5 \\times 4$ matrix, respectively. The test statistic was defined as a ratio of profile likelihoods for the two hypotheses. The method was applied to the CMS measurements. The expected test statistic distribution was estimated twice by generating pseudo-experiments according to both the standard model hypothesis and the single Higgs boson hypothesis best fitting...

  15. Parameterizing Spatial Models of Infectious Disease Transmission that Incorporate Infection Time Uncertainty Using Sampling-Based Likelihood Approximations.

    Directory of Open Access Journals (Sweden)

    Rajat Malik

    Full Text Available A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs, are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD epidemic in the U.K. Our results indicate that substantial computation savings can be obtained--albeit, of course, with some information loss--suggesting that such techniques may be of use in the analysis of very large epidemic data sets.

  16. Empirical likelihood-based confidence intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity.

    Science.gov (United States)

    Gengsheng Qin; Davis, Angela E; Jing, Bing-Yi

    2011-06-01

    For a continuous-scale diagnostic test, it is often of interest to find the range of the sensitivity of the test at the cut-off that yields a desired specificity. In this article, we first define a profile empirical likelihood ratio for the sensitivity of a continuous-scale diagnostic test and show that its limiting distribution is a scaled chi-square distribution. We then propose two new empirical likelihood-based confidence intervals for the sensitivity of the test at a fixed level of specificity by using the scaled chi-square distribution. Simulation studies are conducted to compare the finite sample performance of the newly proposed intervals with the existing intervals for the sensitivity in terms of coverage probability. A real example is used to illustrate the application of the recommended methods.

  17. Likelihood ratio-based differentiation of nodular Hashimoto thyroiditis and papillary thyroid carcinoma in patients with sonographically evident diffuse hashimoto thyroiditis: preliminary study.

    Science.gov (United States)

    Wang, Liang; Xia, Yu; Jiang, Yu-Xin; Dai, Qing; Li, Xiao-Yi

    2012-11-01

    To assess the efficacy of sonography for discriminating nodular Hashimoto thyroiditis from papillary thyroid carcinoma in patients with sonographically evident diffuse Hashimoto thyroiditis. This study included 20 patients with 24 surgically confirmed Hashimoto thyroiditis nodules and 40 patients with 40 papillary thyroid carcinoma nodules; all had sonographically evident diffuse Hashimoto thyroiditis. A retrospective review of the sonograms was performed, and significant benign and malignant sonographic features were selected by univariate and multivariate analyses. The combined likelihood ratio was calculated as the product of each feature's likelihood ratio for papillary thyroid carcinoma. We compared the abilities of the original sonographic features and combined likelihood ratios in diagnosing nodular Hashimoto thyroiditis and papillary thyroid carcinoma by their sensitivity, specificity, and Youden index. The diagnostic capabilities of the sonographic features varied greatly, with Youden indices ranging from 0.175 to 0.700. Compared with single features, combinations of features were unable to improve the Youden indices effectively because the sensitivity and specificity usually changed in opposite directions. For combined likelihood ratios, however, the sensitivity improved greatly without an obvious reduction in specificity, which resulted in the maximum Youden index (0.825). With a combined likelihood ratio greater than 7.00 as the diagnostic criterion for papillary thyroid carcinoma, sensitivity reached 82.5%, whereas specificity remained at 100.0%. With a combined likelihood ratio less than 1.00 for nodular Hashimoto thyroiditis, sensitivity and specificity were 90.0% and 92.5%, respectively. Several sonographic features of nodular Hashimoto thyroiditis and papillary thyroid carcinoma in a background of diffuse Hashimoto thyroiditis were significantly different. The combined likelihood ratio may be superior to original sonographic features for

  18. A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

    Science.gov (United States)

    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.

  19. Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood

    Directory of Open Access Journals (Sweden)

    Olli Saarela

    2012-01-01

    Full Text Available Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-time-to-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example.

  20. A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing

    DEFF Research Database (Denmark)

    Zhu, Yansong; Jha, Abhinav K.; Dreyer, Jakob K.

    2017-01-01

    Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT...... matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate...... and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm-1...

  1. Applications of the Maximum Entropy Method in superspace

    Czech Academy of Sciences Publication Activity Database

    van Smaalen, S.; Palatinus, Lukáš

    2004-01-01

    Roč. 305, - (2004), s. 57-62 ISSN 0015-0193 Grant - others:DFG and FCI(DE) XX Institutional research plan: CEZ:AV0Z1010914 Keywords : Maximum Entropy Method * modulated structures * charge density Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 0.517, year: 2004

  2. A comparative study of the maximum power point tracking methods for PV systems

    International Nuclear Information System (INIS)

    Liu, Yali; Li, Ming; Ji, Xu; Luo, Xi; Wang, Meidi; Zhang, Ying

    2014-01-01

    Highlights: • An improved maximum power point tracking method for PV system was proposed. • Theoretical derivation procedure of the proposed method was provided. • Simulation models of MPPT trackers were established based on MATLAB/Simulink. • Experiments were conducted to verify the effectiveness of the proposed MPPT method. - Abstract: Maximum power point tracking (MPPT) algorithms play an important role in the optimization of the power and efficiency of a photovoltaic (PV) generation system. According to the contradiction of the classical Perturb and Observe (P and Oa) method between the corresponding speed and the tracking accuracy on steady-state, an improved P and O (P and Ob) method has been put forward in this paper by using the Atken interpolation algorithm. To validate the correctness and performance of the proposed method, simulation and experimental study have been implemented. Simulation models of classical P and Oa method and improved P and Ob method have been established by MATLAB/Simulink to analyze each technique under varying solar irradiation and temperature. The experimental results show that the tracking efficiency of P and Ob method is an average of 93% compared to 72% for P and Oa method, this conclusion basically agree with the simulation study. Finally, we proposed the applicable conditions and scope of these MPPT methods in the practical application

  3. A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation.

    Science.gov (United States)

    Meuwly, Didier; Ramos, Daniel; Haraksim, Rudolf

    2017-07-01

    This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likelihood Ratio is used to evaluate the strength of the evidence for a trace specimen, e.g. a fingermark, and a reference specimen, e.g. a fingerprint, to originate from common or different sources. Some theoretical aspects of probabilities necessary for this Guideline were discussed prior to its elaboration, which started after a workshop of forensic researchers and practitioners involved in this topic. In the workshop, the following questions were addressed: "which aspects of a forensic evaluation scenario need to be validated?", "what is the role of the LR as part of a decision process?" and "how to deal with uncertainty in the LR calculation?". The questions: "what to validate?" focuses on the validation methods and criteria and "how to validate?" deals with the implementation of the validation protocol. Answers to these questions were deemed necessary with several objectives. First, concepts typical for validation standards [1], such as performance characteristics, performance metrics and validation criteria, will be adapted or applied by analogy to the LR framework. Second, a validation strategy will be defined. Third, validation methods will be described. Finally, a validation protocol and an example of validation report will be proposed, which can be applied to the forensic fields developing and validating LR methods for the evaluation of the strength of evidence at source level under the following propositions. Copyright © 2016. Published by Elsevier B.V.

  4. Comparison of the accuracy of SST estimates by artificial neural networks (ANN) and other quantitative methods using radiolarian data from the Antarctic and Pacific Oceans

    Digital Repository Service at National Institute of Oceanography (India)

    Gupta, S.M.; Malmgren, B.A.

    ) regression, the maximum likelihood (ML) method, and artificial neural networks (ANNs), based on radiolarian faunal abundance data from surface sediments from the Antarctic and Pacific Oceans. Recent studies have suggested that ANNs may represent one...

  5. The maximum entropy method of moments and Bayesian probability theory

    Science.gov (United States)

    Bretthorst, G. Larry

    2013-08-01

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

  6. Accurate and robust phylogeny estimation based on profile distances: a study of the Chlorophyceae (Chlorophyta

    Directory of Open Access Journals (Sweden)

    Rahmann Sven

    2004-06-01

    Full Text Available Abstract Background In phylogenetic analysis we face the problem that several subclade topologies are known or easily inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum parsimony (MP, neighbor-joining (NJ, or maximum likelihood (ML, nor are they well supported. We represent each subclade by a sequence profile and estimate evolutionary distances between profiles to obtain a matrix of distances between subclades. Results Our estimator of profile distances generalizes the maximum likelihood estimator of sequence distances. The basal branching pattern can be estimated by any distance-based method, such as neighbor-joining. Our method (profile neighbor-joining, PNJ then inherits the accuracy and robustness of profiles and the time efficiency of neighbor-joining. Conclusions Phylogenetic analysis of Chlorophyceae with traditional methods (MP, NJ, ML and MrBayes reveals seven well supported subclades, but the methods disagree on the basal branching pattern. The tree reconstructed by our method is better supported and can be confirmed by known morphological characters. Moreover the accuracy is significantly improved as shown by parametric bootstrap.

  7. Likelihood inference for COM-Poisson cure rate model with interval-censored data and Weibull lifetimes.

    Science.gov (United States)

    Pal, Suvra; Balakrishnan, N

    2017-10-01

    In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right-censored data, the main contribution is in developing the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters of the flexible Conway-Maxwell Poisson cure rate model with Weibull lifetimes. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway-Maxwell Poisson distribution is addressed using the likelihood ratio test and information-based criteria to select a suitable competing cause distribution that provides the best fit to the data. A simulation study is also carried out to demonstrate the loss in efficiency when selecting an improper competing cause distribution which justifies the use of a flexible family of distributions for the number of competing causes. Finally, the proposed methodology and the flexibility of the Conway-Maxwell Poisson distribution are illustrated with two known data sets from the literature: smoking cessation data and breast cosmesis data.

  8. Likelihood ratio sequential sampling models of recognition memory.

    Science.gov (United States)

    Osth, Adam F; Dennis, Simon; Heathcote, Andrew

    2017-02-01

    The mirror effect - a phenomenon whereby a manipulation produces opposite effects on hit and false alarm rates - is benchmark regularity of recognition memory. A likelihood ratio decision process, basing recognition on the relative likelihood that a stimulus is a target or a lure, naturally predicts the mirror effect, and so has been widely adopted in quantitative models of recognition memory. Glanzer, Hilford, and Maloney (2009) demonstrated that likelihood ratio models, assuming Gaussian memory strength, are also capable of explaining regularities observed in receiver-operating characteristics (ROCs), such as greater target than lure variance. Despite its central place in theorising about recognition memory, however, this class of models has not been tested using response time (RT) distributions. In this article, we develop a linear approximation to the likelihood ratio transformation, which we show predicts the same regularities as the exact transformation. This development enabled us to develop a tractable model of recognition-memory RT based on the diffusion decision model (DDM), with inputs (drift rates) provided by an approximate likelihood ratio transformation. We compared this "LR-DDM" to a standard DDM where all targets and lures receive their own drift rate parameters. Both were implemented as hierarchical Bayesian models and applied to four datasets. Model selection taking into account parsimony favored the LR-DDM, which requires fewer parameters than the standard DDM but still fits the data well. These results support log-likelihood based models as providing an elegant explanation of the regularities of recognition memory, not only in terms of choices made but also in terms of the times it takes to make them. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Spurious Latent Class Problem in the Mixed Rasch Model: A Comparison of Three Maximum Likelihood Estimation Methods under Different Ability Distributions

    Science.gov (United States)

    Sen, Sedat

    2018-01-01

    Recent research has shown that over-extraction of latent classes can be observed in the Bayesian estimation of the mixed Rasch model when the distribution of ability is non-normal. This study examined the effect of non-normal ability distributions on the number of latent classes in the mixed Rasch model when estimated with maximum likelihood…

  10. Gaussian copula as a likelihood function for environmental models

    Science.gov (United States)

    Wani, O.; Espadas, G.; Cecinati, F.; Rieckermann, J.

    2017-12-01

    Parameter estimation of environmental models always comes with uncertainty. To formally quantify this parametric uncertainty, a likelihood function needs to be formulated, which is defined as the probability of observations given fixed values of the parameter set. A likelihood function allows us to infer parameter values from observations using Bayes' theorem. The challenge is to formulate a likelihood function that reliably describes the error generating processes which lead to the observed monitoring data, such as rainfall and runoff. If the likelihood function is not representative of the error statistics, the parameter inference will give biased parameter values. Several uncertainty estimation methods that are currently being used employ Gaussian processes as a likelihood function, because of their favourable analytical properties. Box-Cox transformation is suggested to deal with non-symmetric and heteroscedastic errors e.g. for flow data which are typically more uncertain in high flows than in periods with low flows. Problem with transformations is that the results are conditional on hyper-parameters, for which it is difficult to formulate the analyst's belief a priori. In an attempt to address this problem, in this research work we suggest learning the nature of the error distribution from the errors made by the model in the "past" forecasts. We use a Gaussian copula to generate semiparametric error distributions . 1) We show that this copula can be then used as a likelihood function to infer parameters, breaking away from the practice of using multivariate normal distributions. Based on the results from a didactical example of predicting rainfall runoff, 2) we demonstrate that the copula captures the predictive uncertainty of the model. 3) Finally, we find that the properties of autocorrelation and heteroscedasticity of errors are captured well by the copula, eliminating the need to use transforms. In summary, our findings suggest that copulas are an

  11. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  12. Space-Time Chip Equalization for Maximum Diversity Space-Time Block Coded DS-CDMA Downlink Transmission

    Directory of Open Access Journals (Sweden)

    Petré Frederik

    2004-01-01

    Full Text Available In the downlink of DS-CDMA, frequency-selectivity destroys the orthogonality of the user signals and introduces multiuser interference (MUI. Space-time chip equalization is an efficient tool to restore the orthogonality of the user signals and suppress the MUI. Furthermore, multiple-input multiple-output (MIMO communication techniques can result in a significant increase in capacity. This paper focuses on space-time block coding (STBC techniques, and aims at combining STBC techniques with the original single-antenna DS-CDMA downlink scheme. This results into the so-called space-time block coded DS-CDMA downlink schemes, many of which have been presented in the past. We focus on a new scheme that enables both the maximum multiantenna diversity and the maximum multipath diversity. Although this maximum diversity can only be collected by maximum likelihood (ML detection, we pursue suboptimal detection by means of space-time chip equalization, which lowers the computational complexity significantly. To design the space-time chip equalizers, we also propose efficient pilot-based methods. Simulation results show improved performance over the space-time RAKE receiver for the space-time block coded DS-CDMA downlink schemes that have been proposed for the UMTS and IS-2000 W-CDMA standards.

  13. Gamma-ray spectra deconvolution by maximum-entropy methods

    International Nuclear Information System (INIS)

    Los Arcos, J.M.

    1996-01-01

    A maximum-entropy method which includes the response of detectors and the statistical fluctuations of spectra is described and applied to the deconvolution of γ-ray spectra. Resolution enhancement of 25% can be reached for experimental peaks and up to 50% for simulated ones, while the intensities are conserved within 1-2%. (orig.)

  14. Pilot power optimization for AF relaying using maximum likelihood channel estimation

    KAUST Repository

    Wang, Kezhi

    2014-09-01

    Bit error rates (BERs) for amplify-and-forward (AF) relaying systems with two different pilot-symbol-aided channel estimation methods, disintegrated channel estimation (DCE) and cascaded channel estimation (CCE), are derived in Rayleigh fading channels. Based on these BERs, the pilot powers at the source and at the relay are optimized when their total transmitting powers are fixed. Numerical results show that the optimized system has a better performance than other conventional nonoptimized allocation systems. They also show that the optimal pilot power in variable gain is nearly the same as that in fixed gain for similar system settings. andcopy; 2014 IEEE.

  15. Maximum likelihood analysis of bioassay data from long-term follow-up of two refractory PuO2 inhalation cases.

    Science.gov (United States)

    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

  16. Comparsion of maximum viscosity and viscometric method for identification of irradiated sweet potato starch

    International Nuclear Information System (INIS)

    Yi, Sang Duk; Yang, Jae Seung

    2000-01-01

    A study was carried out to compare viscosity and maximum viscosity methods for the detection of irradiated sweet potato starch. The viscosity of all samples decreased by increasing stirring speeds and irradiation doses. This trend was similar for maximum viscosity. Regression coefficients and expressions of viscosity and maximum viscosity with increasing irradiation dose were 0.9823 (y=335.02e -0. 3 366x ) at 120 rpm and 0.9939 (y =-42.544x+730.26). This trend in viscosity was similar for all stirring speeds. Parameter A, B and C values showed a dose dependent relation and were a better parameter for detecting irradiation treatment than maximum viscosity and the viscosity value it self. These results suggest that the detection of irradiated sweet potato starch is possible by both the viscometric and maximum visosity method. Therefore, the authors think that the maximum viscosity method can be proposed as one of the new methods to detect the irradiation treatment for sweet potato starch

  17. Likelihood Estimation of Gamma Ray Bursts Duration Distribution

    OpenAIRE

    Horvath, Istvan

    2005-01-01

    Two classes of Gamma Ray Bursts have been identified so far, characterized by T90 durations shorter and longer than approximately 2 seconds. It was shown that the BATSE 3B data allow a good fit with three Gaussian distributions in log T90. In the same Volume in ApJ. another paper suggested that the third class of GRBs is may exist. Using the full BATSE catalog here we present the maximum likelihood estimation, which gives us 0.5% probability to having only two subclasses. The MC simulation co...

  18. Computation of the Likelihood in Biallelic Diffusion Models Using Orthogonal Polynomials

    Directory of Open Access Journals (Sweden)

    Claus Vogl

    2014-11-01

    Full Text Available In population genetics, parameters describing forces such as mutation, migration and drift are generally inferred from molecular data. Lately, approximate methods based on simulations and summary statistics have been widely applied for such inference, even though these methods waste information. In contrast, probabilistic methods of inference can be shown to be optimal, if their assumptions are met. In genomic regions where recombination rates are high relative to mutation rates, polymorphic nucleotide sites can be assumed to evolve independently from each other. The distribution of allele frequencies at a large number of such sites has been called “allele-frequency spectrum” or “site-frequency spectrum” (SFS. Conditional on the allelic proportions, the likelihoods of such data can be modeled as binomial. A simple model representing the evolution of allelic proportions is the biallelic mutation-drift or mutation-directional selection-drift diffusion model. With series of orthogonal polynomials, specifically Jacobi and Gegenbauer polynomials, or the related spheroidal wave function, the diffusion equations can be solved efficiently. In the neutral case, the product of the binomial likelihoods with the sum of such polynomials leads to finite series of polynomials, i.e., relatively simple equations, from which the exact likelihoods can be calculated. In this article, the use of orthogonal polynomials for inferring population genetic parameters is investigated.

  19. The Prior Can Often Only Be Understood in the Context of the Likelihood

    Directory of Open Access Journals (Sweden)

    Andrew Gelman

    2017-10-01

    Full Text Available A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without reference to the model of the measurement process, but almost all common prior modeling techniques are implicitly motivated by a reference likelihood. In this paper we resolve this apparent paradox by placing the choice of prior into the context of the entire Bayesian analysis, from inference to prediction to model evaluation.

  20. Power spectrum of the geomagnetic field by the maximum entropy method

    International Nuclear Information System (INIS)

    Kantor, I.J.; Trivedi, N.B.

    1980-01-01

    Monthly mean values of Vassouras (state of Rio de Janeiro) geomagnetic field are analyzed us the maximum entropy method. The method is described and compared with other methods of spectral analysis, and its advantages and disadvantages are presented. (Author) [pt

  1. Bootstrap-based Support of HGT Inferred by Maximum Parsimony

    Directory of Open Access Journals (Sweden)

    Nakhleh Luay

    2010-05-01

    Full Text Available Abstract Background Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. Results In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. Conclusions We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/, and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.

  2. Bootstrap-based support of HGT inferred by maximum parsimony.

    Science.gov (United States)

    Park, Hyun Jung; Jin, Guohua; Nakhleh, Luay

    2010-05-05

    Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/), and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.

  3. Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  5. Maximum Power Point Tracking in Variable Speed Wind Turbine Based on Permanent Magnet Synchronous Generator Using Maximum Torque Sliding Mode Control Strategy

    Institute of Scientific and Technical Information of China (English)

    Esmaeil Ghaderi; Hossein Tohidi; Behnam Khosrozadeh

    2017-01-01

    The present study was carried out in order to track the maximum power point in a variable speed turbine by minimizing electromechanical torque changes using a sliding mode control strategy.In this strategy,fhst,the rotor speed is set at an optimal point for different wind speeds.As a result of which,the tip speed ratio reaches an optimal point,mechanical power coefficient is maximized,and wind turbine produces its maximum power and mechanical torque.Then,the maximum mechanical torque is tracked using electromechanical torque.In this technique,tracking error integral of maximum mechanical torque,the error,and the derivative of error are used as state variables.During changes in wind speed,sliding mode control is designed to absorb the maximum energy from the wind and minimize the response time of maximum power point tracking (MPPT).In this method,the actual control input signal is formed from a second order integral operation of the original sliding mode control input signal.The result of the second order integral in this model includes control signal integrity,full chattering attenuation,and prevention from large fluctuations in the power generator output.The simulation results,calculated by using MATLAB/m-file software,have shown the effectiveness of the proposed control strategy for wind energy systems based on the permanent magnet synchronous generator (PMSG).

  6. Maximum entropy method in momentum density reconstruction

    International Nuclear Information System (INIS)

    Dobrzynski, L.; Holas, A.

    1997-01-01

    The Maximum Entropy Method (MEM) is applied to the reconstruction of the 3-dimensional electron momentum density distributions observed through the set of Compton profiles measured along various crystallographic directions. It is shown that the reconstruction of electron momentum density may be reliably carried out with the aid of simple iterative algorithm suggested originally by Collins. A number of distributions has been simulated in order to check the performance of MEM. It is shown that MEM can be recommended as a model-free approach. (author). 13 refs, 1 fig

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

    Science.gov (United States)

    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.

  8. Likelihood updating of random process load and resistance parameters by monitoring

    DEFF Research Database (Denmark)

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2003-01-01

    that maximum likelihood estimation is a rational alternative to an arbitrary weighting for least square fitting. The derived likelihood function gets singularities if the spectrum is prescribed with zero values at some frequencies. This is often the case for models of technically relevant processes......, even though it is of complicated mathematical form, allows an approximate Bayesian updating and control of the time development of the parameters. Some of these parameters can be structural parameters that by too much change reveal progressing damage or other malfunctioning. Thus current process......Spectral parameters for a stationary Gaussian process are most often estimated by Fourier transformation of a realization followed by some smoothing procedure. This smoothing is often a weighted least square fitting of some prespecified parametric form of the spectrum. In this paper it is shown...

  9. Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

    Science.gov (United States)

    Christiansen, Bo

    2015-04-01

    Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

  10. Paired comparisons analysis: an axiomatic approach to ranking methods

    NARCIS (Netherlands)

    Gonzalez-Diaz, J.; Hendrickx, Ruud; Lohmann, E.R.M.A.

    2014-01-01

    In this paper we present an axiomatic analysis of several ranking methods for general tournaments. We find that the ranking method obtained by applying maximum likelihood to the (Zermelo-)Bradley-Terry model, the most common method in statistics and psychology, is one of the ranking methods that

  11. Validation of DNA-based identification software by computation of pedigree likelihood ratios.

    Science.gov (United States)

    Slooten, K

    2011-08-01

    Disaster victim identification (DVI) can be aided by DNA-evidence, by comparing the DNA-profiles of unidentified individuals with those of surviving relatives. The DNA-evidence is used optimally when such a comparison is done by calculating the appropriate likelihood ratios. Though conceptually simple, the calculations can be quite involved, especially with large pedigrees, precise mutation models etc. In this article we describe a series of test cases designed to check if software designed to calculate such likelihood ratios computes them correctly. The cases include both simple and more complicated pedigrees, among which inbred ones. We show how to calculate the likelihood ratio numerically and algebraically, including a general mutation model and possibility of allelic dropout. In Appendix A we show how to derive such algebraic expressions mathematically. We have set up these cases to validate new software, called Bonaparte, which performs pedigree likelihood ratio calculations in a DVI context. Bonaparte has been developed by SNN Nijmegen (The Netherlands) for the Netherlands Forensic Institute (NFI). It is available free of charge for non-commercial purposes (see www.dnadvi.nl for details). Commercial licenses can also be obtained. The software uses Bayesian networks and the junction tree algorithm to perform its calculations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  12. Simultaneous maximum a posteriori longitudinal PET image reconstruction

    Science.gov (United States)

    Ellis, Sam; Reader, Andrew J.

    2017-09-01

    Positron emission tomography (PET) is frequently used to monitor functional changes that occur over extended time scales, for example in longitudinal oncology PET protocols that include routine clinical follow-up scans to assess the efficacy of a course of treatment. In these contexts PET datasets are currently reconstructed into images using single-dataset reconstruction methods. Inspired by recently proposed joint PET-MR reconstruction methods, we propose to reconstruct longitudinal datasets simultaneously by using a joint penalty term in order to exploit the high degree of similarity between longitudinal images. We achieved this by penalising voxel-wise differences between pairs of longitudinal PET images in a one-step-late maximum a posteriori (MAP) fashion, resulting in the MAP simultaneous longitudinal reconstruction (SLR) method. The proposed method reduced reconstruction errors and visually improved images relative to standard maximum likelihood expectation-maximisation (ML-EM) in simulated 2D longitudinal brain tumour scans. In reconstructions of split real 3D data with inserted simulated tumours, noise across images reconstructed with MAP-SLR was reduced to levels equivalent to doubling the number of detected counts when using ML-EM. Furthermore, quantification of tumour activities was largely preserved over a variety of longitudinal tumour changes, including changes in size and activity, with larger changes inducing larger biases relative to standard ML-EM reconstructions. Similar improvements were observed for a range of counts levels, demonstrating the robustness of the method when used with a single penalty strength. The results suggest that longitudinal regularisation is a simple but effective method of improving reconstructed PET images without using resolution degrading priors.

  13. Sur les estimateurs du maximum de vraisemblance dans les mod& ...

    African Journals Online (AJOL)

    Abstract. We are interested in the existence and uniqueness of maximum likelihood estimators of parameters in the two multiplicative regression models, with Poisson or negative binomial probability distributions. Following its work on the multiplicative Poisson model with two factors without repeated measures, Haberman ...

  14. STATIONARITY OF ANNUAL MAXIMUM DAILY STREAMFLOW TIME SERIES IN SOUTH-EAST BRAZILIAN RIVERS

    Directory of Open Access Journals (Sweden)

    Jorge Machado Damázio

    2015-08-01

    Full Text Available DOI: 10.12957/cadest.2014.18302The paper presents a statistical analysis of annual maxima daily streamflow between 1931 and 2013 in South-East Brazil focused in detecting and modelling non-stationarity aspects. Flood protection for the large valleys in South-East Brazil is provided by multiple purpose reservoir systems built during 20th century, which design and operation plans has been done assuming stationarity of historical flood time series. Land cover changes and rapidly-increasing level of atmosphere greenhouse gases of the last century may be affecting flood regimes in these valleys so that it can be that nonstationary modelling should be applied to re-asses dam safety and flood control operation rules at the existent reservoir system. Six annual maximum daily streamflow time series are analysed. The time series were plotted together with fitted smooth loess functions and non-parametric statistical tests are performed to check the significance of apparent trends shown by the plots. Non-stationarity is modelled by fitting univariate extreme value distribution functions which location varies linearly with time. Stationarity and non-stationarity modelling are compared with the likelihood ratio statistic. In four of the six analyzed time series non-stationarity modelling outperformed stationarity modelling.Keywords: Stationarity; Extreme Value Distributions; Flood Frequency Analysis; Maximum Likelihood Method.

  15. 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

    Science.gov (United States)

    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.

  16. Phylogenetic reconstruction methods: an overview.

    Science.gov (United States)

    De Bruyn, Alexandre; Martin, Darren P; Lefeuvre, Pierre

    2014-01-01

    Initially designed to infer evolutionary relationships based on morphological and physiological characters, phylogenetic reconstruction methods have greatly benefited from recent developments in molecular biology and sequencing technologies with a number of powerful methods having been developed specifically to infer phylogenies from macromolecular data. This chapter, while presenting an overview of basic concepts and methods used in phylogenetic reconstruction, is primarily intended as a simplified step-by-step guide to the construction of phylogenetic trees from nucleotide sequences using fairly up-to-date maximum likelihood methods implemented in freely available computer programs. While the analysis of chloroplast sequences from various Vanilla species is used as an illustrative example, the techniques covered here are relevant to the comparative analysis of homologous sequences datasets sampled from any group of organisms.

  17. Empirical projection-based basis-component decomposition method

    Science.gov (United States)

    Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland

    2009-02-01

    Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

  18. Regional analysis of annual maximum rainfall using TL-moments method

    Science.gov (United States)

    Shabri, Ani Bin; Daud, Zalina Mohd; Ariff, Noratiqah Mohd

    2011-06-01

    Information related to distributions of rainfall amounts are of great importance for designs of water-related structures. One of the concerns of hydrologists and engineers is the probability distribution for modeling of regional data. In this study, a novel approach to regional frequency analysis using L-moments is revisited. Subsequently, an alternative regional frequency analysis using the TL-moments method is employed. The results from both methods were then compared. The analysis was based on daily annual maximum rainfall data from 40 stations in Selangor Malaysia. TL-moments for the generalized extreme value (GEV) and generalized logistic (GLO) distributions were derived and used to develop the regional frequency analysis procedure. TL-moment ratio diagram and Z-test were employed in determining the best-fit distribution. Comparison between the two approaches showed that the L-moments and TL-moments produced equivalent results. GLO and GEV distributions were identified as the most suitable distributions for representing the statistical properties of extreme rainfall in Selangor. Monte Carlo simulation was used for performance evaluation, and it showed that the method of TL-moments was more efficient for lower quantile estimation compared with the L-moments.

  19. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  20. Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation

    Directory of Open Access Journals (Sweden)

    Ronald Herrera

    2017-12-01

    Full Text Available 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.