The worst case complexity of maximum parsimony.

Science.gov (United States)

Carmel, Amir; Musa-Lempel, Noa; Tsur, Dekel; Ziv-Ukelson, Michal

2014-11-01

One of the core classical problems in computational biology is that of constructing the most parsimonious phylogenetic tree interpreting an input set of sequences from the genomes of evolutionarily related organisms. We reexamine the classical maximum parsimony (MP) optimization problem for the general (asymmetric) scoring matrix case, where rooted phylogenies are implied, and analyze the worst case bounds of three approaches to MP: The approach of Cavalli-Sforza and Edwards, the approach of Hendy and Penny, and a new agglomerative, "bottom-up" approach we present in this article. We show that the second and third approaches are faster than the first one by a factor of Θ(√n) and Θ(n), respectively, where n is the number of species.

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.

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.

Ancestral sequence reconstruction with Maximum Parsimony

OpenAIRE

Herbst, Lina; Fischer, Mareike

2017-01-01

One of the main aims in phylogenetics is the estimation of ancestral sequences based on present-day data like, for instance, DNA alignments. One way to estimate the data of the last common ancestor of a given set of species is to first reconstruct a phylogenetic tree with some tree inference method and then to use some method of ancestral state inference based on that tree. One of the best-known methods both for tree inference as well as for ancestral sequence inference is Maximum Parsimony (...

Reconstructing phylogenetic networks using maximum parsimony.

Science.gov (United States)

Nakhleh, Luay; Jin, Guohua; Zhao, Fengmei; Mellor-Crummey, John

2005-01-01

Phylogenies - the evolutionary histories of groups of organisms - are one of the most widely used tools throughout the life sciences, as well as objects of research within systematics, evolutionary biology, epidemiology, etc. Almost every tool devised to date to reconstruct phylogenies produces trees; yet it is widely understood and accepted that trees oversimplify the evolutionary histories of many groups of organims, most prominently bacteria (because of horizontal gene transfer) and plants (because of hybrid speciation). Various methods and criteria have been introduced for phylogenetic tree reconstruction. Parsimony is one of the most widely used and studied criteria, and various accurate and efficient heuristics for reconstructing trees based on parsimony have been devised. Jotun Hein suggested a straightforward extension of the parsimony criterion to phylogenetic networks. In this paper we formalize this concept, and provide the first experimental study of the quality of parsimony as a criterion for constructing and evaluating phylogenetic networks. Our results show that, when extended to phylogenetic networks, the parsimony criterion produces promising results. In a great majority of the cases in our experiments, the parsimony criterion accurately predicts the numbers and placements of non-tree events.

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.

A unifying model of genome evolution under parsimony.

Science.gov (United States)

Paten, Benedict; Zerbino, Daniel R; Hickey, Glenn; Haussler, David

2014-06-19

Parsimony and maximum likelihood methods of phylogenetic tree estimation and parsimony methods for genome rearrangements are central to the study of genome evolution yet to date they have largely been pursued in isolation. We present a data structure called a history graph that offers a practical basis for the analysis of genome evolution. It conceptually simplifies the study of parsimonious evolutionary histories by representing both substitutions and double cut and join (DCJ) rearrangements in the presence of duplications. The problem of constructing parsimonious history graphs thus subsumes related maximum parsimony problems in the fields of phylogenetic reconstruction and genome rearrangement. We show that tractable functions can be used to define upper and lower bounds on the minimum number of substitutions and DCJ rearrangements needed to explain any history graph. These bounds become tight for a special type of unambiguous history graph called an ancestral variation graph (AVG), which constrains in its combinatorial structure the number of operations required. We finally demonstrate that for a given history graph G, a finite set of AVGs describe all parsimonious interpretations of G, and this set can be explored with a few sampling moves. This theoretical study describes a model in which the inference of genome rearrangements and phylogeny can be unified under parsimony.

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.

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.

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

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

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.

Mixed integer linear programming for maximum-parsimony phylogeny inference.

Science.gov (United States)

Sridhar, Srinath; Lam, Fumei; Blelloch, Guy E; Ravi, R; Schwartz, Russell

2008-01-01

Reconstruction of phylogenetic trees is a fundamental problem in computational biology. While excellent heuristic methods are available for many variants of this problem, new advances in phylogeny inference will be required if we are to be able to continue to make effective use of the rapidly growing stores of variation data now being gathered. In this paper, we present two integer linear programming (ILP) formulations to find the most parsimonious phylogenetic tree from a set of binary variation data. One method uses a flow-based formulation that can produce exponential numbers of variables and constraints in the worst case. The method has, however, proven extremely efficient in practice on datasets that are well beyond the reach of the available provably efficient methods, solving several large mtDNA and Y-chromosome instances within a few seconds and giving provably optimal results in times competitive with fast heuristics than cannot guarantee optimality. An alternative formulation establishes that the problem can be solved with a polynomial-sized ILP. We further present a web server developed based on the exponential-sized ILP that performs fast maximum parsimony inferences and serves as a front end to a database of precomputed phylogenies spanning the human genome.

Ancestral Sequence Reconstruction with Maximum Parsimony.

Science.gov (United States)

Herbst, Lina; Fischer, Mareike

2017-12-01

One of the main aims in phylogenetics is the estimation of ancestral sequences based on present-day data like, for instance, DNA alignments. One way to estimate the data of the last common ancestor of a given set of species is to first reconstruct a phylogenetic tree with some tree inference method and then to use some method of ancestral state inference based on that tree. One of the best-known methods both for tree inference and for ancestral sequence inference is Maximum Parsimony (MP). In this manuscript, we focus on this method and on ancestral state inference for fully bifurcating trees. In particular, we investigate a conjecture published by Charleston and Steel in 1995 concerning the number of species which need to have a particular state, say a, at a particular site in order for MP to unambiguously return a as an estimate for the state of the last common ancestor. We prove the conjecture for all even numbers of character states, which is the most relevant case in biology. We also show that the conjecture does not hold in general for odd numbers of character states, but also present some positive results for this case.

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.

FPGA Hardware Acceleration of a Phylogenetic Tree Reconstruction with Maximum Parsimony Algorithm

OpenAIRE

BLOCK, Henry; MARUYAMA, Tsutomu

2017-01-01

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel an...

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...

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...

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/.

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.

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.

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.

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

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.

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)

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.

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

Bayesian, Maximum Parsimony and UPGMA Models for Inferring the Phylogenies of Antelopes Using Mitochondrial Markers

OpenAIRE

Khan, Haseeb A.; Arif, Ibrahim A.; Bahkali, Ali H.; Al Farhan, Ahmad H.; Al Homaidan, Ali A.

2008-01-01

This investigation was aimed to compare the inference of antelope phylogenies resulting from the 16S rRNA, cytochrome-b (cyt-b) and d-loop segments of mitochondrial DNA using three different computational models including Bayesian (BA), maximum parsimony (MP) and unweighted pair group method with arithmetic mean (UPGMA). The respective nucleotide sequences of three Oryx species (Oryx leucoryx, Oryx dammah and Oryx gazella) and an out-group (Addax nasomaculatus) were aligned and subjected to B...

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.

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)

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...

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.

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)

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...

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

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...

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.

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.

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...

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.

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.

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

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

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)

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

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

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

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 ...

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.

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.

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

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.

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.

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...

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...

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

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...

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

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

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

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.

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...

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...

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

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...

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

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.

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.

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...

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.

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.

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

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...

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

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

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...

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.

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.

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.

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

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...

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.

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…

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.

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.

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.

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.

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...

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…

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.

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.

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/

Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data.

Science.gov (United States)

O'Reilly, Joseph E; Puttick, Mark N; Parry, Luke; Tanner, Alastair R; Tarver, James E; Fleming, James; Pisani, Davide; Donoghue, Philip C J

2016-04-01

Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction. © 2016 The Authors.

Inferring phylogenetic networks by the maximum parsimony criterion: a case study.

Science.gov (United States)

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

2007-01-01

Horizontal gene transfer (HGT) may result in genes whose evolutionary histories disagree with each other, as well as with the species tree. In this case, reconciling the species and gene trees results in a network of relationships, known as the "phylogenetic network" of the set of species. A phylogenetic network that incorporates HGT consists of an underlying species tree that captures vertical inheritance and a set of edges which model the "horizontal" transfer of genetic material. In a series of papers, Nakhleh and colleagues have recently formulated a maximum parsimony (MP) criterion for phylogenetic networks, provided an array of computationally efficient algorithms and heuristics for computing it, and demonstrated its plausibility on simulated data. In this article, we study the performance and robustness of this criterion on biological data. Our findings indicate that MP is very promising when its application is extended to the domain of phylogenetic network reconstruction and HGT detection. In all cases we investigated, the MP criterion detected the correct number of HGT events required to map the evolutionary history of a gene data set onto the species phylogeny. Furthermore, our results indicate that the criterion is robust with respect to both incomplete taxon sampling and the use of different site substitution matrices. Finally, our results show that the MP criterion is very promising in detecting HGT in chimeric genes, whose evolutionary histories are a mix of vertical and horizontal evolution. Besides the performance analysis of MP, our findings offer new insights into the evolution of 4 biological data sets and new possible explanations of HGT scenarios in their evolutionary history.

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.

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…

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

Parsimonious relevance models

NARCIS (Netherlands)

Meij, E.; Weerkamp, W.; Balog, K.; de Rijke, M.; Myang, S.-H.; Oard, D.W.; Sebastiani, F.; Chua, T.-S.; Leong, M.-K.

2008-01-01

We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test collections in five different genres. Our parsimonious relevance models (i) improve retrieval effectiveness in terms of

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.

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.

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.

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.)

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...

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.

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

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

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.

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.

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.

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)

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...

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...

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...

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.

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

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.

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…

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.

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.

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.

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

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

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.

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)

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.

Seeking parsimony in hydrology and water resources technology

Science.gov (United States)

Koutsoyiannis, D.

2009-04-01

systems to single numbers (a probability or an expected value), and statistics provides the empirical basis of summarizing data, making inference from them, and supporting decision making in water resource management. Unfortunately, the current state of the art in probability, statistics and their union, often called stochastics, is not fully satisfactory for the needs of modelling of hydrological and water resource systems. A first problem is that stochastic modelling has traditionally relied on classical statistics, which is based on the independent "coin-tossing" prototype, rather than on the study of real-world systems whose behaviour is very different from the classical prototype. A second problem is that the stochastic models (particularly the multivariate ones) are often not parsimonious themselves. Therefore, substantial advancement of stochastics is necessary in a new paradigm of parsimonious hydrological modelling. These ideas are illustrated using several examples, namely: (a) hydrological modelling of a karst system in Bosnia and Herzegovina using three different approaches ranging from parsimonious to detailed "physically-based"; (b) parsimonious modelling of a peculiar modified catchment in Greece; (c) a stochastic approach that can replace parameter-excessive ARMA-type models with a generalized algorithm that produces any shape of autocorrelation function (consistent with the accuracy provided by the data) using a couple of parameters; (d) a multivariate stochastic approach which replaces a huge number of parameters estimated from data with coefficients estimated by the principle of maximum entropy; and (e) a parsimonious approach for decision making in multi-reservoir systems using a handful of parameters instead of thousands of decision variables.

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.

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)

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

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

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.

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.

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

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.

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

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

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.

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.

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.

A Multi-Criterion Evolutionary Approach Applied to Phylogenetic Reconstruction

OpenAIRE

Cancino, W.; Delbem, A.C.B.

2010-01-01

In this paper, we proposed an MOEA approach, called PhyloMOEA which solves the phylogenetic inference problem using maximum parsimony and maximum likelihood criteria. The PhyloMOEA's development was motivated by several studies in the literature (Huelsenbeck, 1995; Jin & Nei, 1990; Kuhner & Felsenstein, 1994; Tateno et al., 1994), which point out that various phylogenetic inference methods lead to inconsistent solutions. Techniques using parsimony and likelihood criteria yield to different tr...

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.

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.

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.

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.

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)

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

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...

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.

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.

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...

Maximum Gene-Support Tree

Directory of Open Access Journals (Sweden)

Yunfeng Shan

2008-01-01

Full Text Available Genomes and genes diversify during evolution; however, it is unclear to what extent genes still retain the relationship among species. Model species for molecular phylogenetic studies include yeasts and viruses whose genomes were sequenced as well as plants that have the fossil-supported true phylogenetic trees available. In this study, we generated single gene trees of seven yeast species as well as single gene trees of nine baculovirus species using all the orthologous genes among the species compared. Homologous genes among seven known plants were used for validation of the finding. Four algorithms—maximum parsimony (MP, minimum evolution (ME, maximum likelihood (ML, and neighbor-joining (NJ—were used. Trees were reconstructed before and after weighting the DNA and protein sequence lengths among genes. Rarely a gene can always generate the “true tree” by all the four algorithms. However, the most frequent gene tree, termed “maximum gene-support tree” (MGS tree, or WMGS tree for the weighted one, in yeasts, baculoviruses, or plants was consistently found to be the “true tree” among the species. The results provide insights into the overall degree of divergence of orthologous genes of the genomes analyzed and suggest the following: 1 The true tree relationship among the species studied is still maintained by the largest group of orthologous genes; 2 There are usually more orthologous genes with higher similarities between genetically closer species than between genetically more distant ones; and 3 The maximum gene-support tree reflects the phylogenetic relationship among species in comparison.

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

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).

Parsimonious Language Models for Information Retrieval

NARCIS (Netherlands)

Hiemstra, Djoerd; Robertson, Stephen; Zaragoza, Hugo

We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such,

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)

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...

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/

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

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

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...

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

Efficient parsimony-based methods for phylogenetic network reconstruction.

Science.gov (United States)

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

2007-01-15

Phylogenies--the evolutionary histories of groups of organisms-play a major role in representing relationships among biological entities. Although many biological processes can be effectively modeled as tree-like relationships, others, such as hybrid speciation and horizontal gene transfer (HGT), result in networks, rather than trees, of relationships. Hybrid speciation is a significant evolutionary mechanism in plants, fish and other groups of species. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Maximum parsimony is one of the most commonly used criteria for phylogenetic tree inference. Roughly speaking, inference based on this criterion seeks the tree that minimizes the amount of evolution. In 1990, Jotun Hein proposed using this criterion for inferring the evolution of sequences subject to recombination. Preliminary results on small synthetic datasets. Nakhleh et al. (2005) demonstrated the criterion's application to phylogenetic network reconstruction in general and HGT detection in particular. However, the naive algorithms used by the authors are inapplicable to large datasets due to their demanding computational requirements. Further, no rigorous theoretical analysis of computing the criterion was given, nor was it tested on biological data. In the present work we prove that the problem of scoring the parsimony of a phylogenetic network is NP-hard and provide an improved fixed parameter tractable algorithm for it. Further, we devise efficient heuristics for parsimony-based reconstruction of phylogenetic networks. We test our methods on both synthetic and biological data (rbcL gene in bacteria) and obtain very promising results.

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.

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

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

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.

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...

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

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.

Time-Dependent-Asymmetric-Linear-Parsimonious Ancestral State Reconstruction.

Science.gov (United States)

Didier, Gilles

2017-10-01

The time-dependent-asymmetric-linear parsimony is an ancestral state reconstruction method which extends the standard linear parsimony (a.k.a. Wagner parsimony) approach by taking into account both branch lengths and asymmetric evolutionary costs for reconstructing quantitative characters (asymmetric costs amount to assuming an evolutionary trend toward the direction with the lowest cost). A formal study of the influence of the asymmetry parameter shows that the time-dependent-asymmetric-linear parsimony infers states which are all taken among the known states, except for some degenerate cases corresponding to special values of the asymmetry parameter. This remarkable property holds in particular for the Wagner parsimony. This study leads to a polynomial algorithm which determines, and provides a compact representation of, the parametric reconstruction of a phylogenetic tree, that is for all the unknown nodes, the set of all the possible reconstructed states associated with the asymmetry parameters leading to them. The time-dependent-asymmetric-linear parsimony is finally illustrated with the parametric reconstruction of the body size of cetaceans.

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.

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.

Bayesian, maximum parsimony and UPGMA models for inferring the phylogenies of antelopes using mitochondrial markers.

Science.gov (United States)

Khan, Haseeb A; Arif, Ibrahim A; Bahkali, Ali H; Al Farhan, Ahmad H; Al Homaidan, Ali A

2008-10-06

This investigation was aimed to compare the inference of antelope phylogenies resulting from the 16S rRNA, cytochrome-b (cyt-b) and d-loop segments of mitochondrial DNA using three different computational models including Bayesian (BA), maximum parsimony (MP) and unweighted pair group method with arithmetic mean (UPGMA). The respective nucleotide sequences of three Oryx species (Oryx leucoryx, Oryx dammah and Oryx gazella) and an out-group (Addax nasomaculatus) were aligned and subjected to BA, MP and UPGMA models for comparing the topologies of respective phylogenetic trees. The 16S rRNA region possessed the highest frequency of conserved sequences (97.65%) followed by cyt-b (94.22%) and d-loop (87.29%). There were few transitions (2.35%) and none transversions in 16S rRNA as compared to cyt-b (5.61% transitions and 0.17% transversions) and d-loop (11.57% transitions and 1.14% transversions) while comparing the four taxa. All the three mitochondrial segments clearly differentiated the genus Addax from Oryx using the BA or UPGMA models. The topologies of all the gamma-corrected Bayesian trees were identical irrespective of the marker type. The UPGMA trees resulting from 16S rRNA and d-loop sequences were also identical (Oryx dammah grouped with Oryx leucoryx) to Bayesian trees except that the UPGMA tree based on cyt-b showed a slightly different phylogeny (Oryx dammah grouped with Oryx gazella) with a low bootstrap support. However, the MP model failed to differentiate the genus Addax from Oryx. These findings demonstrate the efficiency and robustness of BA and UPGMA methods for phylogenetic analysis of antelopes using mitochondrial markers.

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…

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...

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.

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.

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.

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…

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.

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)

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.

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...

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...

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

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

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.

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)

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.

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

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.

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

Quality Quandaries- Time Series Model Selection and Parsimony

DEFF Research Database (Denmark)

Bisgaard, Søren; Kulahci, Murat

2009-01-01

Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

Parsimonious Surface Wave Interferometry

KAUST Repository

Li, Jing

2017-10-24

To decrease the recording time of a 2D seismic survey from a few days to one hour or less, we present a parsimonious surface-wave interferometry method. Interferometry allows for the creation of a large number of virtual shot gathers from just two reciprocal shot gathers by crosscoherence of trace pairs, where the virtual surface waves can be inverted for the S-wave velocity model by wave-equation dispersion inversion (WD). Synthetic and field data tests suggest that parsimonious wave-equation dispersion inversion (PWD) gives S-velocity tomograms that are comparable to those obtained from a full survey with a shot at each receiver. The limitation of PWD is that the virtual data lose some information so that the resolution of the S-velocity tomogram can be modestly lower than that of the S-velocity tomogram inverted from a conventional survey.

Parsimonious Surface Wave Interferometry

KAUST Repository

Li, Jing; Hanafy, Sherif; Schuster, Gerard T.

2017-01-01

To decrease the recording time of a 2D seismic survey from a few days to one hour or less, we present a parsimonious surface-wave interferometry method. Interferometry allows for the creation of a large number of virtual shot gathers from just two reciprocal shot gathers by crosscoherence of trace pairs, where the virtual surface waves can be inverted for the S-wave velocity model by wave-equation dispersion inversion (WD). Synthetic and field data tests suggest that parsimonious wave-equation dispersion inversion (PWD) gives S-velocity tomograms that are comparable to those obtained from a full survey with a shot at each receiver. The limitation of PWD is that the virtual data lose some information so that the resolution of the S-velocity tomogram can be modestly lower than that of the S-velocity tomogram inverted from a conventional survey.

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)

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

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.

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.

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.

%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.

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.

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

Ancestral sequence alignment under optimal conditions

Directory of Open Access Journals (Sweden)

Brown Daniel G

2005-11-01

Full Text Available Abstract Background Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences. Results We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang. Conclusion We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the

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.

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

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.

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.

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.

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.)

Parsimonious Refraction Interferometry and Tomography

KAUST Repository

Hanafy, Sherif; Schuster, Gerard T.

2017-01-01

We present parsimonious refraction interferometry and tomography where a densely populated refraction data set can be obtained from two reciprocal and several infill shot gathers. The assumptions are that the refraction arrivals are head waves

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.

Systematics and morphological evolution within the moss family Bryaceae: a comparison between parsimony and Bayesian methods for reconstruction of ancestral character states.

Science.gov (United States)

Pedersen, Niklas; Holyoak, David T; Newton, Angela E

2007-06-01

The Bryaceae are a large cosmopolitan moss family including genera of significant morphological and taxonomic complexity. Phylogenetic relationships within the Bryaceae were reconstructed based on DNA sequence data from all three genomic compartments. In addition, maximum parsimony and Bayesian inference were employed to reconstruct ancestral character states of 38 morphological plus four habitat characters and eight insertion/deletion events. The recovered phylogenetic patterns are generally in accord with previous phylogenies based on chloroplast DNA sequence data and three major clades are identified. The first clade comprises Bryum bornholmense, B. rubens, B. caespiticium, and Plagiobryum. This corroborates the hypothesis suggested by previous studies that several Bryum species are more closely related to Plagiobryum than to the core Bryum species. The second clade includes Acidodontium, Anomobryum, and Haplodontium, while the third clade contains the core Bryum species plus Imbribryum. Within the latter clade, B. subapiculatum and B. tenuisetum form the sister clade to Imbribryum. Reconstructions of ancestral character states under maximum parsimony and Bayesian inference suggest fourteen morphological synapomorphies for the ingroup and synapomorphies are detected for most clades within the ingroup. Maximum parsimony and Bayesian reconstructions of ancestral character states are mostly congruent although Bayesian inference shows that the posterior probability of ancestral character states may decrease dramatically when node support is taken into account. Bayesian inference also indicates that reconstructions may be ambiguous at internal nodes for highly polymorphic characters.

A parsimonious dynamic model for river water quality assessment.

Science.gov (United States)

Mannina, Giorgio; Viviani, Gaspare

2010-01-01

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH(4), and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management.

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.

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.

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.

Phylogenetic analyses of RPB1 and RPB2 support a middle Cretaceous origin for a clade comprising all agriculturally and medically important fusaria

DEFF Research Database (Denmark)

O’Donnell, Kerry; Rooney, Alejandro P.; Proctor, Robert H.

2013-01-01

Fusarium (Hypocreales, Nectriaceae) is one of the most economically important and systematically challenging groups of mycotoxigenic phytopathogens and emergent human pathogens. We conducted maximum likelihood (ML), maximum parsimony (MP) and Bayesian (B) analyses on partial DNA-directed RNA poly...

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.

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.

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.

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

DEFF Research Database (Denmark)

Andersen, Tobias

2015-01-01

integration to speech perception along with three model variations. In early MLE, integration is based on a continuous internal representation before categorization, which can make the model more parsimonious by imposing constraints that reflect experimental designs. The study also shows that cross......Speech perception is facilitated by seeing the articulatory mouth movements of the talker. This is due to perceptual audiovisual integration, which also causes the McGurk−MacDonald illusion, and for which a comprehensive computational account is still lacking. Decades of research have largely......-validation can evaluate models of audiovisual integration based on typical data sets taking both goodness-of-fit and model flexibility into account. All models were tested on a published data set previously used for testing the FLMP. Cross-validation favored the early MLE while more conventional error measures...

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

Morphological character evolution of Amorphophallus (Araceae) based on a combined phylogenetic analysis of trnL, rbcL, and LEAFY second intron sequences

NARCIS (Netherlands)

Sedayu, A.; Eurlings, M.C.M.; Gravendeel, B.; Hetterscheid, W.L.A.

2010-01-01

Sequences of three different genes in 69 taxa of Amorphophallus were combined to reconstruct the molecular phylogeny of this species-rich Aroid genus. The data set was analyzed by three different methods, Maximum Parsimony, Maximum Likelihood and Bayesian analysis, producing slightly different tree

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

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.

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.

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.

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.

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...

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.

A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion

Science.gov (United States)

2013-01-01

Background Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T∗for a set H of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj∈H, the sum of the edge weights belonging to the path from hi to hj in T∗ is not smaller than the observed number of changes between hi and hj. Finding the most parsimonious phylogeny for H involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions. Results In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove

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

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.

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.

Parsimonious Refraction Interferometry and Tomography

KAUST Repository

Hanafy, Sherif

2017-02-04

We present parsimonious refraction interferometry and tomography where a densely populated refraction data set can be obtained from two reciprocal and several infill shot gathers. The assumptions are that the refraction arrivals are head waves, and a pair of reciprocal shot gathers and several infill shot gathers are recorded over the line of interest. Refraction traveltimes from these shot gathers are picked and spawned into O(N2) virtual refraction traveltimes generated by N virtual sources, where N is the number of geophones in the 2D survey. The virtual traveltimes can be inverted to give the velocity tomogram. This enormous increase in the number of traveltime picks and associated rays, compared to the many fewer traveltimes from the reciprocal and infill shot gathers, allows for increased model resolution and a better condition number with the system of normal equations. A significant benefit is that the parsimonious survey and the associated traveltime picking is far less time consuming than that for a standard refraction survey with a dense distribution of sources.

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.

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.

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

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

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

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.

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.

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

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.].

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

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.

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)

Plethodontid salamander mitochondrial genomics: A parsimonyevaluation of character conflict and implications for historicalbiogeography

Energy Technology Data Exchange (ETDEWEB)

Macey, J. Robert

2005-01-19

A new parsimony analysis of 27 complete mitochondrial genomic sequences is conducted to investigate the phylogenetic relationships of plethodontid salamanders. This analysis focuses on the amount of character conflict between phylogenetic trees recovered from newly conducted parsimony searches and the Bayesian and maximum likelihood topology reported by Mueller et al. (2004, PNAS, 101, 13820-13825). Strong support for Hemidactylium as the sister taxon to all other plethodontids is recovered from parsimony analyses. Plotting area relationships on the most parsimonious phylogenetic tree suggests that eastern North America is the origin of the family Plethodontidae supporting the ''Out of Appalachia'' hypothesis. A new taxonomy that recognizes clades recovered from phylogenetic analyses is proposed.

Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria.

Science.gov (United States)

Padial, José M; Grant, Taran; Frost, Darrel R

2014-06-26

Brachycephaloidea is a monophyletic group of frogs with more than 1000 species distributed throughout the New World tropics, subtropics, and Andean regions. Recently, the group has been the target of multiple molecular phylogenetic analyses, resulting in extensive changes in its taxonomy. Here, we test previous hypotheses of phylogenetic relationships for the group by combining available molecular evidence (sequences of 22 genes representing 431 ingroup and 25 outgroup terminals) and performing a tree-alignment analysis under the parsimony optimality criterion using the program POY. To elucidate the effects of alignment and optimality criterion on phylogenetic inferences, we also used the program MAFFT to obtain a similarity-alignment for analysis under both parsimony and maximum likelihood using the programs TNT and GARLI, respectively. Although all three analytical approaches agreed on numerous points, there was also extensive disagreement. Tree-alignment under parsimony supported the monophyly of the ingroup and the sister group relationship of the monophyletic marsupial frogs (Hemiphractidae), while maximum likelihood and parsimony analyses of the MAFFT similarity-alignment did not. All three methods differed with respect to the position of Ceuthomantis smaragdinus (Ceuthomantidae), with tree-alignment using parsimony recovering this species as the sister of Pristimantis + Yunganastes. All analyses rejected the monophyly of Strabomantidae and Strabomantinae as originally defined, and the tree-alignment analysis under parsimony further rejected the recently redefined Craugastoridae and Pristimantinae. Despite the greater emphasis in the systematics literature placed on the choice of optimality criterion for evaluating trees than on the choice of method for aligning DNA sequences, we found that the topological differences attributable to the alignment method were as great as those caused by the optimality criterion. Further, the optimal tree-alignment indicates

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.

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

Inferring Phylogenetic Networks Using PhyloNet.

Science.gov (United States)

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

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....

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.

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.

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

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.

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

Science.gov (United States)

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

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...

Phylogeny of Salsoleae s.l. (Chenopodiaceae) based on DNA sequence data from ITS, psbB-psbH, and rbcL, with emphasis on taxa of northwestern China

Science.gov (United States)

Zhi-Bin Wen; Ming-Li Zhang; Ge-Lin Zhu; Stewart C. Sanderson

2010-01-01

To reconstruct phylogeny and verify the monophyly of major subgroups, a total of 52 species representing almost all species of Salsoleae s.l. in China were sampled, with analysis based on three molecular markers (nrDNA ITS, cpDNA psbB-psbH and rbcL), using maximum parsimony, maximum likelihood, and Bayesian inference methods. Our molecular evidence provides strong...

Parsimonious refraction interferometry

KAUST Repository

Hanafy, Sherif

2016-09-06

We present parsimonious refraction interferometry where a densely populated refraction data set can be obtained from just two shot gathers. The assumptions are that the first arrivals are comprised of head waves and direct waves, and a pair of reciprocal shot gathers is recorded over the line of interest. The refraction traveltimes from these reciprocal shot gathers can be picked and decomposed into O(N2) refraction traveltimes generated by N virtual sources, where N is the number of geophones in the 2D survey. This enormous increase in the number of virtual traveltime picks and associated rays, compared to the 2N traveltimes from the two reciprocal shot gathers, allows for increased model resolution and better condition numbers in the normal equations. Also, a reciprocal survey is far less time consuming than a standard refraction survey with a dense distribution of sources.

Parsimonious refraction interferometry

KAUST Repository

Hanafy, Sherif; Schuster, Gerard T.

2016-01-01

We present parsimonious refraction interferometry where a densely populated refraction data set can be obtained from just two shot gathers. The assumptions are that the first arrivals are comprised of head waves and direct waves, and a pair of reciprocal shot gathers is recorded over the line of interest. The refraction traveltimes from these reciprocal shot gathers can be picked and decomposed into O(N2) refraction traveltimes generated by N virtual sources, where N is the number of geophones in the 2D survey. This enormous increase in the number of virtual traveltime picks and associated rays, compared to the 2N traveltimes from the two reciprocal shot gathers, allows for increased model resolution and better condition numbers in the normal equations. Also, a reciprocal survey is far less time consuming than a standard refraction survey with a dense distribution of sources.

A simplified parsimonious higher order multivariate Markov chain model

Science.gov (United States)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

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.

A tridiagonal parsimonious higher order multivariate Markov chain model

Science.gov (United States)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

An ITS phylogeny of Leccinum and an analysis of the evolution of minisatellite-like sequences within ITS1

NARCIS (Netherlands)

Bakker, den H.C.; Gravendeel, B.; Kuyper, T.W.

2004-01-01

Phylogenetic relationships of the European species of Leccinum (Boletales, Boletaceae) were investigated by maximum parsimony, Bayesian and likelihood analyses of nrITS1-5.8S-ITS2 and 28S sequences. The separate gene trees inferred were largely concordant, and their combined analysis indicates that

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

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

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

Phylogenetic relationships among populations of Pristurus rupestris Blanford,1874 (Sauria: Sphaerodactylidae) in southern Iran

OpenAIRE

YOUSOFI, SUGOL; POUYANI, ESKANDAR RASTEGAR; HOJATI, VIDA

2015-01-01

We examined intraspecific relationships of the subspecies Pristurus rupestris iranicus from the northern Persian Gulf area (Hormozgan, Bushehr, and Sistan and Baluchestan provinces). Phylogenetic relationships among these samples were estimated based on the mitochondrial cytochrome b gene. We used three methods of phylogenetic tree reconstruction (maximum likelihood, maximum parsimony, and Bayesian inference). The sampled populations were divided into 5 clades but exhibit little genetic diver...

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.

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.

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

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.

Principle of Parsimony, Fake Science, and Scales

Science.gov (United States)

Yeh, T. C. J.; Wan, L.; Wang, X. S.

2017-12-01

Considering difficulties in predicting exact motions of water molecules, and the scale of our interests (bulk behaviors of many molecules), Fick's law (diffusion concept) has been created to predict solute diffusion process in space and time. G.I. Taylor (1921) demonstrated that random motion of the molecules reach the Fickian regime in less a second if our sampling scale is large enough to reach ergodic condition. Fick's law is widely accepted for describing molecular diffusion as such. This fits the definition of the parsimony principle at the scale of our concern. Similarly, advection-dispersion or convection-dispersion equation (ADE or CDE) has been found quite satisfactory for analysis of concentration breakthroughs of solute transport in uniformly packed soil columns. This is attributed to the solute is often released over the entire cross-section of the column, which has sampled many pore-scale heterogeneities and met the ergodicity assumption. Further, the uniformly packed column contains a large number of stationary pore-size heterogeneity. The solute thus reaches the Fickian regime after traveling a short distance along the column. Moreover, breakthrough curves are concentrations integrated over the column cross-section (the scale of our interest), and they meet the ergodicity assumption embedded in the ADE and CDE. To the contrary, scales of heterogeneity in most groundwater pollution problems evolve as contaminants travel. They are much larger than the scale of our observations and our interests so that the ergodic and the Fickian conditions are difficult. Upscaling the Fick's law for solution dispersion, and deriving universal rules of the dispersion to the field- or basin-scale pollution migrations are merely misuse of the parsimony principle and lead to a fake science ( i.e., the development of theories for predicting processes that can not be observed.) The appropriate principle of parsimony for these situations dictates mapping of large

Parsimonious wave-equation travel-time inversion for refraction waves

KAUST Repository

Fu, Lei; Hanafy, Sherif M.; Schuster, Gerard T.

2017-01-01

We present a parsimonious wave-equation travel-time inversion technique for refraction waves. A dense virtual refraction dataset can be generated from just two reciprocal shot gathers for the sources at the endpoints of the survey line, with N

A class representative model for Pure Parsimony Haplotyping under uncertain data.

Directory of Open Access Journals (Sweden)

Daniele Catanzaro

Full Text Available The Pure Parsimony Haplotyping (PPH problem is a NP-hard combinatorial optimization problem that consists of finding the minimum number of haplotypes necessary to explain a given set of genotypes. PPH has attracted more and more attention in recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from mapping complex disease genes to inferring population histories, passing through designing drugs, functional genomics and pharmacogenetics. In this article we investigate, for the first time, a recent version of PPH called the Pure Parsimony Haplotype problem under Uncertain Data (PPH-UD. This version mainly arises when the input genotypes are not accurate, i.e., when some single nucleotide polymorphisms are missing or affected by errors. We propose an exact approach to solution of PPH-UD based on an extended version of Catanzaro et al.[1] class representative model for PPH, currently the state-of-the-art integer programming model for PPH. The model is efficient, accurate, compact, polynomial-sized, easy to implement, solvable with any solver for mixed integer programming, and usable in all those cases for which the parsimony criterion is well suited for haplotype estimation.

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...

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.

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.

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

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.

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.

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

Parsimonious wave-equation travel-time inversion for refraction waves

KAUST Repository

Fu, Lei

2017-02-14

We present a parsimonious wave-equation travel-time inversion technique for refraction waves. A dense virtual refraction dataset can be generated from just two reciprocal shot gathers for the sources at the endpoints of the survey line, with N geophones evenly deployed along the line. These two reciprocal shots contain approximately 2N refraction travel times, which can be spawned into O(N2) refraction travel times by an interferometric transformation. Then, these virtual refraction travel times are used with a source wavelet to create N virtual refraction shot gathers, which are the input data for wave-equation travel-time inversion. Numerical results show that the parsimonious wave-equation travel-time tomogram has about the same accuracy as the tomogram computed by standard wave-equation travel-time inversion. The most significant benefit is that a reciprocal survey is far less time consuming than the standard refraction survey where a source is excited at each geophone location.

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 ...

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.

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.

Phylogenetic Analysis of a ?Jewel Orchid? Genus Goodyera (Orchidaceae) Based on DNA Sequence Data from Nuclear and Plastid Regions

OpenAIRE

Hu, Chao; Tian, Huaizhen; Li, Hongqing; Hu, Aiqun; Xing, Fuwu; Bhattacharjee, Avishek; Hsu, Tianchuan; Kumar, Pankaj; Chung, Shihwen

2016-01-01

A molecular phylogeny of Asiatic species of Goodyera (Orchidaceae, Cranichideae, Goodyerinae) based on the nuclear ribosomal internal transcribed spacer (ITS) region and two chloroplast loci (matK and trnL-F) was presented. Thirty-five species represented by 132 samples of Goodyera were analyzed, along with other 27 genera/48 species, using Pterostylis longifolia and Chloraea gaudichaudii as outgroups. Bayesian inference, maximum parsimony and maximum likelihood methods were used to reveal th...

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....

[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.

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.

Live phylogeny with polytomies: Finding the most compact parsimonious trees.

Science.gov (United States)

Papamichail, D; Huang, A; Kennedy, E; Ott, J-L; Miller, A; Papamichail, G

2017-08-01

Construction of phylogenetic trees has traditionally focused on binary trees where all species appear on leaves, a problem for which numerous efficient solutions have been developed. Certain application domains though, such as viral evolution and transmission, paleontology, linguistics, and phylogenetic stemmatics, often require phylogeny inference that involves placing input species on ancestral tree nodes (live phylogeny), and polytomies. These requirements, despite their prevalence, lead to computationally harder algorithmic solutions and have been sparsely examined in the literature to date. In this article we prove some unique properties of most parsimonious live phylogenetic trees with polytomies, and their mapping to traditional binary phylogenetic trees. We show that our problem reduces to finding the most compact parsimonious tree for n species, and describe a novel efficient algorithm to find such trees without resorting to exhaustive enumeration of all possible tree topologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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

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.

Mosasauroid phylogeny under multiple phylogenetic methods provides new insights on the evolution of aquatic adaptations in the group.

Directory of Open Access Journals (Sweden)

Tiago R Simões

Full Text Available Mosasauroids were a successful lineage of squamate reptiles (lizards and snakes that radiated during the Late Cretaceous (95-66 million years ago. They can be considered one of the few lineages in the evolutionary history of tetrapods to have acquired a fully aquatic lifestyle, similarly to whales, ichthyosaurs and plesiosaurs. Despite a long history of research on this group, their phylogenetic relationships have only been tested so far using traditional (unweighted maximum parsimony. However, hypotheses of mosasauroid relationships and the recently proposed multiple origins of aquatically adapted pelvic and pedal features in this group can be more thoroughly tested by methods that take into account variation in branch lengths and evolutionary rates. In this study, we present the first mosasauroid phylogenetic analysis performed under different analytical methods, including maximum likelihood, Bayesian inference, and implied weighting maximum parsimony. The results indicate a lack of congruence in the topological position of halisaurines and Dallasaurus. Additionally, the genus Prognathodon is paraphyletic under all hypotheses. Interestingly, a number of traditional mosasauroid clades become weakly supported, or unresolved, under Bayesian analyses. The reduced resolutions in some consensus trees create ambiguities concerning the evolution of fully aquatic pelvic/pedal conditions under many analyses. However, when enough resolution was obtained, reversals of the pelvic/pedal conditions were favoured by parsimony and likelihood ancestral state reconstructions instead of independent origins of aquatic features in mosasauroids. It is concluded that most of the observed discrepancies among the results can be associated with different analytical procedures, but also due to limited postcranial data on halisaurines, yaguarasaurines and Dallasaurus.

Mosasauroid phylogeny under multiple phylogenetic methods provides new insights on the evolution of aquatic adaptations in the group

Science.gov (United States)

Vernygora, Oksana; Paparella, Ilaria; Jimenez-Huidobro, Paulina; Caldwell, Michael W.

2017-01-01

Mosasauroids were a successful lineage of squamate reptiles (lizards and snakes) that radiated during the Late Cretaceous (95–66 million years ago). They can be considered one of the few lineages in the evolutionary history of tetrapods to have acquired a fully aquatic lifestyle, similarly to whales, ichthyosaurs and plesiosaurs. Despite a long history of research on this group, their phylogenetic relationships have only been tested so far using traditional (unweighted) maximum parsimony. However, hypotheses of mosasauroid relationships and the recently proposed multiple origins of aquatically adapted pelvic and pedal features in this group can be more thoroughly tested by methods that take into account variation in branch lengths and evolutionary rates. In this study, we present the first mosasauroid phylogenetic analysis performed under different analytical methods, including maximum likelihood, Bayesian inference, and implied weighting maximum parsimony. The results indicate a lack of congruence in the topological position of halisaurines and Dallasaurus. Additionally, the genus Prognathodon is paraphyletic under all hypotheses. Interestingly, a number of traditional mosasauroid clades become weakly supported, or unresolved, under Bayesian analyses. The reduced resolutions in some consensus trees create ambiguities concerning the evolution of fully aquatic pelvic/pedal conditions under many analyses. However, when enough resolution was obtained, reversals of the pelvic/pedal conditions were favoured by parsimony and likelihood ancestral state reconstructions instead of independent origins of aquatic features in mosasauroids. It is concluded that most of the observed discrepancies among the results can be associated with different analytical procedures, but also due to limited postcranial data on halisaurines, yaguarasaurines and Dallasaurus. PMID:28467456

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)

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.

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

Molecular phylogeny of ateline new world monkeys (Platyrrhini, atelinae) based on gamma-globin gene sequences: evidence that brachyteles is the sister group of lagothrix.

Science.gov (United States)

Meireles, C M; Czelusniak, J; Schneider, M P; Muniz, J A; Brigido, M C; Ferreira, H S; Goodman, M

1999-06-01

Nucleotide sequences, each spanning approximately 7 kb of the contiguous gamma1 and gamma2 globin genomic loci, were determined for seven species representing all extant genera (Ateles, Lagothrix, Brachyteles, and Alouatta) of the New World monkey subfamily Atelinae. After aligning these seven ateline sequences with outgroup sequences from several other primate (non-ateline) genera, they were analyzed by maximum parsimony, maximum likelihood, and neighbor-joining algorithms. All three analyzes estimated the same phylogenetic relationships: [Alouatta [Ateles (Brachyteles, Lagothrix)

[Reconstruction of the phylogenetic position of larch (Larix sukaczewii Dylis) by sequencing data for the trnK intron of chloroplast DNA].

Science.gov (United States)

Bashalkhanov, S I; Konstantinov, Iu M; Verbitskiĭ, D S; Kobzev, V F

2003-10-01

To reconstruct the systematic relationships of larch Larix sukaczewii, we used the chloroplast trnK intron sequences of L. decidua, L. sukaczewii, L. sibirica, L. czekanovskii, and L. gmelinii. Analysis of phylogenetic trees constructed using the maximum parsimony and maximum likelihood methods showed a clear divergence of the trnK intron sequences between L. sukaczewii and L. sibirica. This divergence reaches intraspecific level, which supports a previously published hypothesis on the taxonomic isolation of L. sukaczewii.

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

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°.

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.

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.

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.

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

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

Time-Lapse Monitoring of Subsurface Fluid Flow using Parsimonious Seismic Interferometry

KAUST Repository

Hanafy, Sherif; Li, Jing; Schuster, Gerard T.

2017-01-01

of parsimonious seismic interferometry with the time-lapse mentoring idea with field examples, where we were able to record 30 different data sets within a 2-hour period. The recorded data are then processed to generate 30 snapshots that shows the spread of water

DupTree: a program for large-scale phylogenetic analyses using gene tree parsimony.

Science.gov (United States)

Wehe, André; Bansal, Mukul S; Burleigh, J Gordon; Eulenstein, Oliver

2008-07-01

DupTree is a new software program for inferring rooted species trees from collections of gene trees using the gene tree parsimony approach. The program implements a novel algorithm that significantly improves upon the run time of standard search heuristics for gene tree parsimony, and enables the first truly genome-scale phylogenetic analyses. In addition, DupTree allows users to examine alternate rootings and to weight the reconciliation costs for gene trees. DupTree is an open source project written in C++. DupTree for Mac OS X, Windows, and Linux along with a sample dataset and an on-line manual are available at http://genome.cs.iastate.edu/CBL/DupTree

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.

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.

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.

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.

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.

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

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....

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

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.

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...

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.

A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history.

Science.gov (United States)

Cherry, Joshua L

2017-02-23

Maximum compatibility is a method of phylogenetic reconstruction that is seldom applied to molecular sequences. It may be ideal for certain applications, such as reconstructing phylogenies of closely-related bacteria on the basis of whole-genome sequencing. Here I present an algorithm that rapidly computes phylogenies according to a compatibility criterion. Although based on solutions to the maximum clique problem, this algorithm deals properly with ambiguities in the data. The algorithm is applied to bacterial data sets containing up to nearly 2000 genomes with several thousand variable nucleotide sites. Run times are several seconds or less. Computational experiments show that maximum compatibility is less sensitive than maximum parsimony to the inclusion of nucleotide data that, though derived from actual sequence reads, has been identified as likely to be misleading. Maximum compatibility is a useful tool for certain phylogenetic problems, such as inferring the relationships among closely-related bacteria from whole-genome sequence data. The algorithm presented here rapidly solves fairly large problems of this type, and provides robustness against misleading characters than can pollute large-scale sequencing data.

Calculating the probability of multitaxon evolutionary trees: bootstrappers Gambit.

OpenAIRE

Lake, J A

1995-01-01

The reconstruction of multitaxon trees from molecular sequences is confounded by the variety of algorithms and criteria used to evaluate trees, making it difficult to compare the results of different analyses. A global method of multitaxon phylogenetic reconstruction described here, Bootstrappers Gambit, can be used with any four-taxon algorithm, including distance, maximum likelihood, and parsimony methods. It incorporates a Bayesian-Jeffreys'-bootstrap analysis to provide a uniform probabil...

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.

Consequence Valuing as Operation and Process: A Parsimonious Analysis of Motivation

Science.gov (United States)

Whelan, Robert; Barnes-Holmes, Dermot

2010-01-01

The concept of the motivating operation (MO) has been subject to 3 criticisms: (a) the terms and concepts employed do not always overlap with traditional behavior-analytic verbal practices; (b) the dual nature of the MO is unclear; and (c) there is a lack of adequate contact with empirical data. We offer a more parsimonious approach to motivation,…

Phylogenetic relationships of click beetles (Coleoptera: Elateridae) inferred from 28S ribosomal DNA: insights into the evolution of bioluminescence in Elateridae.

Science.gov (United States)

Sagegami-Oba, Reiko; Oba, Yuichi; Ohira, Hitoo

2007-02-01

Although the taxonomy of click beetles (family Elateridae) has been studied extensively, inconsistencies remain. We examine here the relationships between species of Elateridae based on partial sequences of nuclear 28S ribosomal DNA. Specimens were collected primarily from Japan, while luminous click beetles were also sampled from Central and South America to investigate the origins of bioluminescence in Elateridae. Neighbor-joining, maximum-parsimony, and maximum-likelihood analyses produced a consistent basal topology with high statistical support that is partially congruent with the results of previous investigations based on the morphological characteristics of larvae and adults. The most parsimonious reconstruction of the "luminous" and "nonluminous" states, based on the present molecular phylogeny, indicates that the ancestral state of Elateridae was nonluminous. This suggests that the bioluminescence in click beetle evolved independent of that of other luminous beetles, such as Lampyridae, despite their common mechanisms of bioluminescence.

On defining a unique phylogenetic tree with homoplastic characters.

Science.gov (United States)

Goloboff, Pablo A; Wilkinson, Mark

2018-05-01

This paper discusses the problem of whether creating a matrix with all the character state combinations that have a fixed number of steps (or extra steps) on a given tree T, produces the same tree T when analyzed with maximum parsimony or maximum likelihood. Exhaustive enumeration of cases up to 20 taxa for binary characters, and up to 12 taxa for 4-state characters, shows that the same tree is recovered (as unique most likely or most parsimonious tree) as long as the number of extra steps is within 1/4 of the number of taxa. This dependence, 1/4 of the number of taxa, is discussed with a general argumentation, in terms of the spread of the character changes on the tree used to select character state distributions. The present finding allows creating matrices which have as much homoplasy as possible for the most parsimonious or likely tree to be predictable, and examination of these matrices with hill-climbing search algorithms provides additional evidence on the (lack of a) necessary relationship between homoplasy and the ability of search methods to find optimal trees. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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)

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.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

Science.gov (United States)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series

Directory of Open Access Journals (Sweden)

Fernando Luiz Cyrino Oliveira

2014-01-01

Full Text Available The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p, one for each period (month of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.

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

The plunge in German electricity futures prices – Analysis using a parsimonious fundamental model

International Nuclear Information System (INIS)

Kallabis, Thomas; Pape, Christian; Weber, Christoph

2016-01-01

The German market has seen a plunge in wholesale electricity prices from 2007 until 2014, with base futures prices dropping by more than 40%. This is frequently attributed to the unexpected high increase in renewable power generation. Using a parsimonious fundamental model, we determine the respective impact of supply and demand shocks on electricity futures prices. The used methodology is based on a piecewise linear approximation of the supply stack and time-varying price-inelastic demand. This parsimonious model is able to replicate electricity futures prices and discover non-linear dependencies in futures price formation. We show that emission prices have a higher impact on power prices than renewable penetration. Changes in renewables, demand and installed capacities turn out to be similarly important for explaining the decrease in operation margins of conventional power plants. We thus argue for the establishment of an independent authority to stabilize emission prices. - Highlights: •We build a parsimonious fundamental model based on a piecewise linear bid stack. •We use the model to investigate impact factors for the plunge in German futures prices. •Largest impact by CO_2 price developments followed by demand and renewable feed-in. •Power plant operating profits strongly affected by demand and renewables. •We argue that stabilizing CO_2 emission prices could provide better market signals.

Ancestral sequence reconstruction in primate mitochondrial DNA: compositional bias and effect on functional inference.

Science.gov (United States)

Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D

2004-10-01

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and

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

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...

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...

Méthodes avancées pour la résolution du problème de maximum parcimonie

OpenAIRE

Vazquez Ortiz , Karla Esmeralda

2016-01-01

Phylogenetic reconstruction is considered a central underpinning of diverse fields like ecology, molecular biology and physiology where genealogical relationships of species or gene sequences represented as trees can provide the most meaningful insights into biology. Maximum Parsimony (MP) is an important approach to solve the phylogenetic reconstruction based on an optimality criterion under which the tree that minimizes the total number of genetic transformations is preferred. In this thesi...

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.

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

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).

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.

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.

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....

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

Quality Assurance Based on Descriptive and Parsimonious Appearance Models

DEFF Research Database (Denmark)

Nielsen, Jannik Boll; Eiríksson, Eyþór Rúnar; Kristensen, Rasmus Lyngby

2015-01-01

In this positional paper, we discuss the potential benefits of using appearance models in additive manufacturing, metal casting, wind turbine blade production, and 3D content acquisition. Current state of the art in acquisition and rendering of appearance cannot easily be used for quality assurance...... in these areas. The common denominator is the need for descriptive and parsimonious appearance models. By ‘parsimonious’ we mean with few parameters so that a model is useful both for fast acquisition, robust fitting, and fast rendering of appearance. The word ‘descriptive’ refers to the fact that a model should...

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.

Molecular Phylogenetics: Mathematical Framework and Unsolved Problems

Science.gov (United States)

Xia, Xuhua

Phylogenetic relationship is essential in dating evolutionary events, reconstructing ancestral genes, predicting sites that are important to natural selection, and, ultimately, understanding genomic evolution. Three categories of phylogenetic methods are currently used: the distance-based, the maximum parsimony, and the maximum likelihood method. Here, I present the mathematical framework of these methods and their rationales, provide computational details for each of them, illustrate analytically and numerically the potential biases inherent in these methods, and outline computational challenges and unresolved problems. This is followed by a brief discussion of the Bayesian approach that has been recently used in molecular phylogenetics.

Phylogenetic Trees From Sequences

Science.gov (United States)

Ryvkin, Paul; Wang, Li-San

In this chapter, we review important concepts and approaches for phylogeny reconstruction from sequence data.We first cover some basic definitions and properties of phylogenetics, and briefly explain how scientists model sequence evolution and measure sequence divergence. We then discuss three major approaches for phylogenetic reconstruction: distance-based phylogenetic reconstruction, maximum parsimony, and maximum likelihood. In the third part of the chapter, we review how multiple phylogenies are compared by consensus methods and how to assess confidence using bootstrapping. At the end of the chapter are two sections that list popular software packages and additional reading.

Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy

DEFF Research Database (Denmark)

Callot, Laurent; Kristensen, Johannes Tang

the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule.We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response...

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.

Hypothesis of the Disappearance of the Limits of Improvidence and Parsimony in the Function of Consumption in an Islamic Economy

Directory of Open Access Journals (Sweden)

محمد أحمد حسن الأفندي

2018-04-01

Full Text Available There is a rich literature about the analysis of consumption behavior from the perspective of Islamic economy. The focus of such literature has been on the incorporation of the effect of moral values on individuals’ consumption behavior. However, studies on consumption did not pay enough heed to the analysis of the ultimate effect of faith values on the track of consumption behavior over time. This desired track of consumption involves showing certain hypotheses and probabilities. This study suggests a normative statement which includes the gradual disappearance of parsimony and improvidence over time. This disappearance would correct the deviation of actual consumption of society members from the desired moderate consumption level, so as to make households’ consumption behavior at the desired level which is consistent with Islamic Sharia. The study emphasizes the need to develop analysis and research in two integrated directions: i conducting more empirical studies to examine the consistency of the normative statement with evidence from real situations, and ii conducting more analysis to develop a specific measure for the desired consumption levels as well as the limits of parsimony and improvidence. Keywords: Disappearance of improvidence and parsimony limits, Desired moderate consumption level, Actual consumption, Improvidence and parsimony consumption levels, Track of households’ consumption behavior.

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

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.

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...

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

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.

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.

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...

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.

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

The conquering of North America: dated phylogenetic and biogeographic inference of migratory behavior in bee hummingbirds.

Science.gov (United States)

Licona-Vera, Yuyini; Ornelas, Juan Francisco

2017-06-05

Geographical and temporal patterns of diversification in bee hummingbirds (Mellisugini) were assessed with respect to the evolution of migration, critical for colonization of North America. We generated a dated multilocus phylogeny of the Mellisugini based on a dense sampling using Bayesian inference, maximum-likelihood and maximum parsimony methods, and reconstructed the ancestral states of distributional areas in a Bayesian framework and migratory behavior using maximum parsimony, maximum-likelihood and re-rooting methods. All phylogenetic analyses confirmed monophyly of the Mellisugini and the inclusion of Atthis, Calothorax, Doricha, Eulidia, Mellisuga, Microstilbon, Myrmia, Tilmatura, and Thaumastura. Mellisugini consists of two clades: (1) South American species (including Tilmatura dupontii), and (2) species distributed in North and Central America and the Caribbean islands. The second clade consists of four subclades: Mexican (Calothorax, Doricha) and Caribbean (Archilochus, Calliphlox, Mellisuga) sheartails, Calypte, and Selasphorus (incl. Atthis). Coalescent-based dating places the origin of the Mellisugini in the mid-to-late Miocene, with crown ages of most subclades in the early Pliocene, and subsequent species splits in the Pleistocene. Bee hummingbirds reached western North America by the end of the Miocene and the ancestral mellisuginid (bee hummingbirds) was reconstructed as sedentary, with four independent gains of migratory behavior during the evolution of the Mellisugini. Early colonization of North America and subsequent evolution of migration best explained biogeographic and diversification patterns within the Mellisugini. The repeated evolution of long-distance migration by different lineages was critical for the colonization of North America, contributing to the radiation of bee hummingbirds. Comparative phylogeography is needed to test whether the repeated evolution of migration resulted from northward expansion of southern sedentary

Beyond technology acceptance to effective technology use: a parsimonious and actionable model.

Science.gov (United States)

Holahan, Patricia J; Lesselroth, Blake J; Adams, Kathleen; Wang, Kai; Church, Victoria

2015-05-01

To develop and test a parsimonious and actionable model of effective technology use (ETU). Cross-sectional survey of primary care providers (n = 53) in a large integrated health care organization that recently implemented new medication reconciliation technology. Surveys assessed 5 technology-related perceptions (compatibility with work values, implementation climate, compatibility with work processes, perceived usefulness, and ease of use) and 1 outcome variable, ETU. ETU was measured as both consistency and quality of technology use. Compatibility with work values and implementation climate were found to have differential effects on consistency and quality of use. When implementation climate was strong, consistency of technology use was high. However, quality of technology use was high only when implementation climate was strong and values compatibility was high. This is an important finding and highlights the importance of users' workplace values as a key determinant of quality of use. To extend our effectiveness in implementing new health care information technology, we need parsimonious models that include actionable determinants of ETU and account for the differential effects of these determinants on the multiple dimensions of ETU. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Quantitative Phylogenetic Analysis in the 21st Century Análisis Filogenéticos Cuantitativos en el siglo XXI

Directory of Open Access Journals (Sweden)

Daniel R Brooks

2007-12-01

Full Text Available We review Hennigian phylogenetics and compare it with Maximum parsimony, Maximum likelihood, and Bayesian likelihood approaches. All methods use the principle of parsimony in some form. Hennigian-based approaches are justified ontologically by the Darwinian concepts of phylogenetic conservatism and cohesion of homologies, embodied in Hennig's Auxiliary Principle, and applied by outgroup comparisons. Parsimony is used as an epistemological tool, applied a posteriori to choose the most robust hypothesis when there are conflicting data. Quantitative methods use parsimony as an ontological criterion: Maximum parsimony analysis uses unweighted parsimony, Maximum likelihood weight all characters equally that explain the data, and Bayesian likelihood relying on weighting each character partition that explains the data. Different results most often stem from insufficient data, in which case each quantitative method treats ambiguities differently. All quantitative methods produce networks. The networks can be converted into trees by rooting them. If the rooting is done in accordance with Hennig's Auxiliary Principle, using outgroup comparisons, the resulting tree can then be interpreted as a phylogenetic hypothesis. As the size of the data set increases, likelihood methods select models that allow an increasingly greater number of a priori possibilities, converging on the Hennigian perspective that nothing is prohibited a priori. Thus, all methods produce similar results, regardless of data type, especially when their networks are rooted using outgroups. Appeals to Popperian philosophy cannot justify any kind of phylogenetic analysis, because they argue from effect to cause rather than from cause to effect. Nor can particular methods be justified on the basis of statistical consistency, because all may be consistent or inconsistent depending on the data. If analyses using different types of data and/or different methods of phylogeny reconstruction do not

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.

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

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.

Type I STS markers are more informative than cytochrome B in phylogenetic reconstruction of the Mustelidae (Mammalia: Carnivora).

Science.gov (United States)

Koepfli, Klaus-Peter; Wayne, Robert K

2003-10-01

We compared the utility of five nuclear gene segments amplified with type I sequence-tagged site (STS) primers versus the complete mitochondrial cytochrome b (cyt b) gene in resolving phylogenetic relationships within the Mustelidae, a large and ecomorphologically diverse family of mammalian carnivores. Maximum parsimony and likelihood analyses of separate and combined data sets were used to address questions regarding the levels of homoplasy, incongruence, and information content within and among loci. All loci showed limited resolution in the separate analyses because of either a low amount of informative variation (nuclear genes) or high levels of homoplasy (cyt b). Individually or combined, the nuclear gene sequences had less homoplasy, retained more signal, and were more decisive, even though cyt b contained more potentially informative variation than all the nuclear sequences combined. We obtained a well-resolved and supported phylogeny when the nuclear sequences were combined. Maximum likelihood and Bayesian phylogenetic analyses of the total combined data (nuclear and mitochondrial DNA sequences) were able to better accommodate the high levels of homoplasy in the cyt b data than was an equally weighted maximum parsimony analysis. Furthermore, partition Bremer support analyses of the total combined tree showed that the relative support of the nuclear and mitochondrial genes differed according to whether or not the homoplasy in the cyt b gene was downweighted. Although the cyt b gene contributed phylogenetic signal for most major groupings, the nuclear gene sequences were more effective in reconstructing the deeper nodes of the combined tree in the equally weighted parsimony analysis, as judged by the variable-length bootstrap method. The total combined data supported the monophyly of the Lutrinae (otters), whereas the Melinae (badgers) and Mustelinae (weasels, martens) were both paraphyletic. The American badger, Taxidea taxus (Taxidiinae), was the most

phangorn: phylogenetic analysis in R.

Science.gov (United States)

Schliep, Klaus Peter

2011-02-15

phangorn is a package for phylogenetic reconstruction and analysis in the R language. Previously it was only possible to estimate phylogenetic trees with distance methods in R. phangorn, now offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses. phangorn can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. phangorn is licensed under GPL 2.

Replicate phylogenies and post-glacial range expansion of the pitcher-plant mosquito, Wyeomyia smithii, in North America.

Directory of Open Access Journals (Sweden)

Clayton Merz

Full Text Available Herein we tested the repeatability of phylogenetic inference based on high throughput sequencing by increased taxon sampling using our previously published techniques in the pitcher-plant mosquito, Wyeomyia smithii in North America. We sampled 25 natural populations drawn from different localities nearby 21 previous collection localities and used these new data to construct a second, independent phylogeny, expressly to test the reproducibility of phylogenetic patterns. Comparison of trees between the two data sets based on both maximum parsimony and maximum likelihood with Bayesian posterior probabilities showed close correspondence in the grouping of the most southern populations into clear clades. However, discrepancies emerged, particularly in the middle of W. smithii's current range near the previous maximum extent of the Laurentide Ice Sheet, especially concerning the most recent common ancestor to mountain and northern populations. Combining all 46 populations from both studies into a single maximum parsimony tree and taking into account the post-glacial historical biogeography of associated flora provided an improved picture of W. smithii's range expansion in North America. In a more general sense, we propose that extensive taxon sampling, especially in areas of known geological disruption is key to a comprehensive approach to phylogenetics that leads to biologically meaningful phylogenetic inference.

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

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

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.

Assessing internet addiction using the parsimonious internet addiction components model—A preliminary study.

OpenAIRE

Kuss, D.J.; Shorter, G.W.; Rooij, A.J. van; Griffiths, M.D.; Schoenmakers, T.M.

2014-01-01

Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (Journal ...

Molecular phylogeny and larval morphological diversity of the lanternfish genus Hygophum (Teleostei: Myctophidae).

Science.gov (United States)

Yamaguchi, M; Miya, M; Okiyama, M; Nishida, M

2000-04-01

Larvae of the deep-sea lanternfish genus Hygophum (Myctophidae) exhibit a remarkable morphological diversity that is quite unexpected, considering their homogeneous adult morphology. In an attempt to elucidate the evolutionary patterns of such larval morphological diversity, nucleotide sequences of a portion of the mitochondrially encoded 16S ribosomal RNA gene were determined for seven Hygophum species and three outgroup taxa. Secondary structure-based alignment resulted in a character matrix consisting of 1172 bp of unambiguously aligned sequences, which were subjected to phylogenetic analyses using maximum-parsimony, maximum-likelihood, and neighbor-joining methods. The resultant tree topologies from the three methods were congruent, with most nodes, including that of the genus Hygophum, being strongly supported by various tree statistics. The most parsimonious reconstruction of the three previously recognized, distinct larval morphs onto the molecular phylogeny revealed that one of the morphs had originated as the common ancestor of the genus, the other two having diversified separately in two subsequent major clades. The patterns of such diversification are discussed in terms of the unusual larval eye morphology and geographic distribution. Copyright 2000 Academic Press.

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

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...

Things fall apart: biological species form unconnected parsimony networks.

Science.gov (United States)

Hart, Michael W; Sunday, Jennifer

2007-10-22

The generality of operational species definitions is limited by problematic definitions of between-species divergence. A recent phylogenetic species concept based on a simple objective measure of statistically significant genetic differentiation uses between-species application of statistical parsimony networks that are typically used for population genetic analysis within species. Here we review recent phylogeographic studies and reanalyse several mtDNA barcoding studies using this method. We found that (i) alignments of DNA sequences typically fall apart into a separate subnetwork for each Linnean species (but with a higher rate of true positives for mtDNA data) and (ii) DNA sequences from single species typically stick together in a single haplotype network. Departures from these patterns are usually consistent with hybridization or cryptic species diversity.

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 ...

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...

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

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.

A large version of the small parsimony problem

DEFF Research Database (Denmark)

Fredslund, Jakob; Hein, Jotun; Scharling, Tejs

2003-01-01

the most parsimonious assignment of nucleotides. The gaps of the alignment are represented in a so-called gap graph, and through theoretically sound preprocessing the graph is reduced to pave the way for a running time which in all but the most pathological examples is far better than the exponential worst......Given a multiple alignment over $k$ sequences, an evolutionary tree relating the sequences, and a subadditive gap penalty function (e.g. an affine function), we reconstruct the internal nodes of the tree optimally: we find the optimal explanation in terms of indels of the observed gaps and find...... case time. E.g. for a tree with nine leaves and a random alignment of length 10.000 with 60% gaps, the running time is on average around 45 seconds. For a real alignment of length 9868 of nine HIV-1 sequences, the running time is less than one second....

Phylogenetic analyses of Vitis (Vitaceae) based on complete chloroplast genome sequences: effects of taxon sampling and phylogenetic methods on resolving relationships among rosids.

Science.gov (United States)

Jansen, Robert K; Kaittanis, Charalambos; Saski, Christopher; Lee, Seung-Bum; Tomkins, Jeffrey; Alverson, Andrew J; Daniell, Henry

2006-04-09

The Vitaceae (grape) is an economically important family of angiosperms whose phylogenetic placement is currently unresolved. Recent phylogenetic analyses based on one to several genes have suggested several alternative placements of this family, including sister to Caryophyllales, asterids, Saxifragales, Dilleniaceae or to rest of rosids, though support for these different results has been weak. There has been a recent interest in using complete chloroplast genome sequences for resolving phylogenetic relationships among angiosperms. These studies have clarified relationships among several major lineages but they have also emphasized the importance of taxon sampling and the effects of different phylogenetic methods for obtaining accurate phylogenies. We sequenced the complete chloroplast genome of Vitis vinifera and used these data to assess relationships among 27 angiosperms, including nine taxa of rosids. The Vitis vinifera chloroplast genome is 160,928 bp in length, including a pair of inverted repeats of 26,358 bp that are separated by small and large single copy regions of 19,065 bp and 89,147 bp, respectively. The gene content and order of Vitis is identical to many other unrearranged angiosperm chloroplast genomes, including tobacco. Phylogenetic analyses using maximum parsimony and maximum likelihood were performed on DNA sequences of 61 protein-coding genes for two datasets with 28 or 29 taxa, including eight or nine taxa from four of the seven currently recognized major clades of rosids. Parsimony and likelihood phylogenies of both data sets provide strong support for the placement of Vitaceae as sister to the remaining rosids. However, the position of the Myrtales and support for the monophyly of the eurosid I clade differs between the two data sets and the two methods of analysis. In parsimony analyses, the inclusion of Gossypium is necessary to obtain trees that support the monophyly of the eurosid I clade. However, maximum likelihood analyses place

Phylogenetic analyses of Vitis (Vitaceae based on complete chloroplast genome sequences: effects of taxon sampling and phylogenetic methods on resolving relationships among rosids

Directory of Open Access Journals (Sweden)

Alverson Andrew J

2006-04-01

Full Text Available Abstract Background The Vitaceae (grape is an economically important family of angiosperms whose phylogenetic placement is currently unresolved. Recent phylogenetic analyses based on one to several genes have suggested several alternative placements of this family, including sister to Caryophyllales, asterids, Saxifragales, Dilleniaceae or to rest of rosids, though support for these different results has been weak. There has been a recent interest in using complete chloroplast genome sequences for resolving phylogenetic relationships among angiosperms. These studies have clarified relationships among several major lineages but they have also emphasized the importance of taxon sampling and the effects of different phylogenetic methods for obtaining accurate phylogenies. We sequenced the complete chloroplast genome of Vitis vinifera and used these data to assess relationships among 27 angiosperms, including nine taxa of rosids. Results The Vitis vinifera chloroplast genome is 160,928 bp in length, including a pair of inverted repeats of 26,358 bp that are separated by small and large single copy regions of 19,065 bp and 89,147 bp, respectively. The gene content and order of Vitis is identical to many other unrearranged angiosperm chloroplast genomes, including tobacco. Phylogenetic analyses using maximum parsimony and maximum likelihood were performed on DNA sequences of 61 protein-coding genes for two datasets with 28 or 29 taxa, including eight or nine taxa from four of the seven currently recognized major clades of rosids. Parsimony and likelihood phylogenies of both data sets provide strong support for the placement of Vitaceae as sister to the remaining rosids. However, the position of the Myrtales and support for the monophyly of the eurosid I clade differs between the two data sets and the two methods of analysis. In parsimony analyses, the inclusion of Gossypium is necessary to obtain trees that support the monophyly of the eurosid I clade

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.

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

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

Comparison of Boolean analysis and standard phylogenetic methods using artificially evolved and natural mt-tRNA sequences from great apes.

Science.gov (United States)

Ari, Eszter; Ittzés, Péter; Podani, János; Thi, Quynh Chi Le; Jakó, Eena

2012-04-01

Boolean analysis (or BOOL-AN; Jakó et al., 2009. BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction. Mol. Phylogenet. Evol. 52, 887-97.), a recently developed method for sequence comparison uses the Iterative Canonical Form of Boolean functions. It considers sequence information in a way entirely different from standard phylogenetic methods (i.e. Maximum Parsimony, Maximum-Likelihood, Neighbor-Joining, and Bayesian analysis). The performance and reliability of Boolean analysis were tested and compared with the standard phylogenetic methods, using artificially evolved - simulated - nucleotide sequences and the 22 mitochondrial tRNA genes of the great apes. At the outset, we assumed that the phylogeny of Hominidae is generally well established, and the guide tree of artificial sequence evolution can also be used as a benchmark. These offer a possibility to compare and test the performance of different phylogenetic methods. Trees were reconstructed by each method from 2500 simulated sequences and 22 mitochondrial tRNA sequences. We also introduced a special re-sampling method for Boolean analysis on permuted sequence sites, the P-BOOL-AN procedure. Considering the reliability values (branch support values of consensus trees and Robinson-Foulds distances) we used for simulated sequence trees produced by different phylogenetic methods, BOOL-AN appeared as the most reliable method. Although the mitochondrial tRNA sequences of great apes are relatively short (59-75 bases long) and the ratio of their constant characters is about 75%, BOOL-AN, P-BOOL-AN and the Bayesian approach produced the same tree-topology as the established phylogeny, while the outcomes of Maximum Parsimony, Maximum-Likelihood and Neighbor-Joining methods were equivocal. We conclude that Boolean analysis is a promising alternative to existing methods of sequence comparison for phylogenetic reconstruction and congruence analysis. Copyright © 2012 Elsevier Inc. All

A comparison of maximum entropy and maximum likelihood estimation

NARCIS (Netherlands)

Oude Lansink, A.G.J.M.

1999-01-01

Gegevens betreffende het ondernemerschap op Nederlandse akkerbouwbedrijven zijn in 2 benaderingsmethodes verwerkt, welke onderling op voorspellende nauwkeurigheid en op prijs-elasticiteit zijn vergeleken

Assessing Internet addiction using the parsimonious Internet addiction components model - a preliminary study [forthcoming

OpenAIRE

Kuss, DJ; Shorter, GW; Van Rooij, AJ; Griffiths, MD; Schoenmakers, T

2014-01-01

Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (2005), i...

Seeing the elephant: Parsimony, functionalism, and the emergent design of contempt and other sentiments.

Science.gov (United States)

Gervais, Matthew M; Fessler, Daniel M T

2017-01-01

The target article argues that contempt is a sentiment, and that sentiments are the deep structure of social affect. The 26 commentaries meet these claims with a range of exciting extensions and applications, as well as critiques. Most significantly, we reply that construction and emergence are necessary for, not incompatible with, evolved design, while parsimony requires explanatory adequacy and predictive accuracy, not mere simplicity.

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.

Pholcid spider molecular systematics revisited, with new insights into the biogeography and the evolution of the group

DEFF Research Database (Denmark)

Dimitrov, Dimitar Stefanov; Astrin, Jonas J.; Huber, Bernhard A.

2013-01-01

analysed using parsimony, maximum-likelihood and Bayesian methods for phylogenetic reconstruction. We show that in several previously problematic cases molecular and morphological data are converging towards a single hypothesis. This is also the first study that explicitly addresses the age of pholcid......We analysed seven genetic markers sampled from 165 pholcids and 34 outgroups in order to test and improve the recently revised classification of the family. Our results are based on the largest and most comprehensive set of molecular data so far to study pholcid relationships. The data were...

A Parsimonious Instrument for Predicting Students' Intent to Pursue a Sales Career: Scale Development and Validation

Science.gov (United States)

Peltier, James W.; Cummins, Shannon; Pomirleanu, Nadia; Cross, James; Simon, Rob

2014-01-01

Students' desire and intention to pursue a career in sales continue to lag behind industry demand for sales professionals. This article develops and validates a reliable and parsimonious scale for measuring and predicting student intention to pursue a selling career. The instrument advances previous scales in three ways. The instrument is…

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

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.

New approaches to phylogenetic tree search and their application to large numbers of protein alignments.

Science.gov (United States)

Whelan, Simon

2007-10-01

Phylogenetic tree estimation plays a critical role in a wide variety of molecular studies, including molecular systematics, phylogenetics, and comparative genomics. Finding the optimal tree relating a set of sequences using score-based (optimality criterion) methods, such as maximum likelihood and maximum parsimony, may require all possible trees to be considered, which is not feasible even for modest numbers of sequences. In practice, trees are estimated using heuristics that represent a trade-off between topological accuracy and speed. I present a series of novel algorithms suitable for score-based phylogenetic tree reconstruction that demonstrably improve the accuracy of tree estimates while maintaining high computational speeds. The heuristics function by allowing the efficient exploration of large numbers of trees through novel hill-climbing and resampling strategies. These heuristics, and other computational approximations, are implemented for maximum likelihood estimation of trees in the program Leaphy, and its performance is compared to other popular phylogenetic programs. Trees are estimated from 4059 different protein alignments using a selection of phylogenetic programs and the likelihoods of the tree estimates are compared. Trees estimated using Leaphy are found to have equal to or better likelihoods than trees estimated using other phylogenetic programs in 4004 (98.6%) families and provide a unique best tree that no other program found in 1102 (27.1%) families. The improvement is particularly marked for larger families (80 to 100 sequences), where Leaphy finds a unique best tree in 81.7% of families.

The performance of phylogenetic algorithms in estimating haplotype genealogies with migration.

Science.gov (United States)

Salzburger, Walter; Ewing, Greg B; Von Haeseler, Arndt

2011-05-01

Genealogies estimated from haplotypic genetic data play a prominent role in various biological disciplines in general and in phylogenetics, population genetics and phylogeography in particular. Several software packages have specifically been developed for the purpose of reconstructing genealogies from closely related, and hence, highly similar haplotype sequence data. Here, we use simulated data sets to test the performance of traditional phylogenetic algorithms, neighbour-joining, maximum parsimony and maximum likelihood in estimating genealogies from nonrecombining haplotypic genetic data. We demonstrate that these methods are suitable for constructing genealogies from sets of closely related DNA sequences with or without migration. As genealogies based on phylogenetic reconstructions are fully resolved, but not necessarily bifurcating, and without reticulations, these approaches outperform widespread 'network' constructing methods. In our simulations of coalescent scenarios involving panmictic, symmetric and asymmetric migration, we found that phylogenetic reconstruction methods performed well, while the statistical parsimony approach as implemented in TCS performed poorly. Overall, parsimony as implemented in the PHYLIP package performed slightly better than other methods. We further point out that we are not making the case that widespread 'network' constructing methods are bad, but that traditional phylogenetic tree finding methods are applicable to haplotypic data and exhibit reasonable performance with respect to accuracy and robustness. We also discuss some of the problems of converting a tree to a haplotype genealogy, in particular that it is nonunique. © 2011 Blackwell Publishing Ltd.

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.

Parsimonious Ways to Use Vision for Navigation

Directory of Open Access Journals (Sweden)

Paul Graham

2012-05-01

Full Text Available The use of visual information for navigation appears to be a universal strategy for sighted animals, amongst which, one particular group of expert navigators are the ants. The broad interest in studies of ant navigation is in part due to their small brains, thus biomimetic engineers expect to be impressed by elegant control solutions, and psychologists might hope for a description of the minimal cognitive requirements for complex spatial behaviours. In this spirit, we have been taking an interdisciplinary approach to the visual guided navigation of ants in their natural habitat. Behavioural experiments and natural image statistics show that visual navigation need not depend on the remembering or recognition of objects. Further modelling work suggests how simple behavioural routines might enable navigation using familiarity detection rather than explicit recall, and we present a proof of concept that visual navigation using familiarity can be achieved without specifying when or what to learn, nor separating routes into sequences of waypoints. We suggest that our current model represents the only detailed and complete model of insect route guidance to date. What's more, we believe the suggested mechanisms represent useful parsimonious hypotheses for the visually guided navigation in larger-brain animals.

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...

The scenario on the origin of translation in the RNA world: in principle of replication parsimony

Directory of Open Access Journals (Sweden)

Ma Wentao

2010-11-01

Full Text Available Abstract Background It is now believed that in the origin of life, proteins should have been "invented" in an RNA world. However, due to the complexity of a possible RNA-based proto-translation system, this evolving process seems quite complicated and the associated scenario remains very blurry. Considering that RNA can bind amino acids with specificity, it has been reasonably supposed that initial peptides might have been synthesized on "RNA templates" containing multiple amino acid binding sites. This "Direct RNA Template (DRT" mechanism is attractive because it should be the simplest mechanism for RNA to synthesize peptides, thus very likely to have been adopted initially in the RNA world. Then, how this mechanism could develop into a proto-translation system mechanism is an interesting problem. Presentation of the hypothesis Here an explanation to this problem is shown considering the principle of "replication parsimony" --- genetic information tends to be utilized in a parsimonious way under selection pressure, due to its replication cost (e.g., in the RNA world, nucleotides and ribozymes for RNA replication. Because a DRT would be quite long even for a short peptide, its replication cost would be great. Thus the diversity and the length of functional peptides synthesized by the DRT mechanism would be seriously limited. Adaptors (proto-tRNAs would arise to allow a DRT's complementary strand (called "C-DRT" here to direct the synthesis of the same peptide synthesized by the DRT itself. Because the C-DRT is a necessary part in the DRT's replication, fewer turns of the DRT's replication would be needed to synthesize definite copies of the functional peptide, thus saving the replication cost. Acting through adaptors, C-DRTs could transform into much shorter templates (called "proto-mRNAs" here and substitute the role of DRTs, thus significantly saving the replication cost. A proto-rRNA corresponding to the small subunit rRNA would then emerge

Convergence among cave catfishes: long-branch attraction and a Bayesian relative rates test.

Science.gov (United States)

Wilcox, T P; García de León, F J; Hendrickson, D A; Hillis, D M

2004-06-01

Convergence has long been of interest to evolutionary biologists. Cave organisms appear to be ideal candidates for studying convergence in morphological, physiological, and developmental traits. Here we report apparent convergence in two cave-catfishes that were described on morphological grounds as congeners: Prietella phreatophila and Prietella lundbergi. We collected mitochondrial DNA sequence data from 10 species of catfishes, representing five of the seven genera in Ictaluridae, as well as seven species from a broad range of siluriform outgroups. Analysis of the sequence data under parsimony supports a monophyletic Prietella. However, both maximum-likelihood and Bayesian analyses support polyphyly of the genus, with P. lundbergi sister to Ictalurus and P. phreatophila sister to Ameiurus. The topological difference between parsimony and the other methods appears to result from long-branch attraction between the Prietella species. Similarly, the sequence data do not support several other relationships within Ictaluridae supported by morphology. We develop a new Bayesian method for examining variation in molecular rates of evolution across a phylogeny.

A new mathematical modeling for pure parsimony haplotyping problem.

Science.gov (United States)

Feizabadi, R; Bagherian, M; Vaziri, H R; Salahi, M

2016-11-01

Pure parsimony haplotyping (PPH) problem is important in bioinformatics because rational haplotyping inference plays important roles in analysis of genetic data, mapping complex genetic diseases such as Alzheimer's disease, heart disorders and etc. Haplotypes and genotypes are m-length sequences. Although several integer programing models have already been presented for PPH problem, its NP-hardness characteristic resulted in ineffectiveness of those models facing the real instances especially instances with many heterozygous sites. In this paper, we assign a corresponding number to each haplotype and genotype and based on those numbers, we set a mixed integer programing model. Using numbers, instead of sequences, would lead to less complexity of the new model in comparison with previous models in a way that there are neither constraints nor variables corresponding to heterozygous nucleotide sites in it. Experimental results approve the efficiency of the new model in producing better solution in comparison to two state-of-the art haplotyping approaches. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

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

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...

Hide and vanish: data sets where the most parsimonious tree is known but hard to find, and their implications for tree search methods.

Science.gov (United States)

Goloboff, Pablo A

2014-10-01

Three different types of data sets, for which the uniquely most parsimonious tree can be known exactly but is hard to find with heuristic tree search methods, are studied. Tree searches are complicated more by the shape of the tree landscape (i.e. the distribution of homoplasy on different trees) than by the sheer abundance of homoplasy or character conflict. Data sets of Type 1 are those constructed by Radel et al. (2013). Data sets of Type 2 present a very rugged landscape, with narrow peaks and valleys, but relatively low amounts of homoplasy. For such a tree landscape, subjecting the trees to TBR and saving suboptimal trees produces much better results when the sequence of clipping for the tree branches is randomized instead of fixed. An unexpected finding for data sets of Types 1 and 2 is that starting a search from a random tree instead of a random addition sequence Wagner tree may increase the probability that the search finds the most parsimonious tree; a small artificial example where these probabilities can be calculated exactly is presented. Data sets of Type 3, the most difficult data sets studied here, comprise only congruent characters, and a single island with only one most parsimonious tree. Even if there is a single island, missing entries create a very flat landscape which is difficult to traverse with tree search algorithms because the number of equally parsimonious trees that need to be saved and swapped to effectively move around the plateaus is too large. Minor modifications of the parameters of tree drifting, ratchet, and sectorial searches allow travelling around these plateaus much more efficiently than saving and swapping large numbers of equally parsimonious trees with TBR. For these data sets, two new related criteria for selecting taxon addition sequences in Wagner trees (the "selected" and "informative" addition sequences) produce much better results than the standard random or closest addition sequences. These new methods for Wagner

The Impact of Reconstruction Methods, Phylogenetic Uncertainty and Branch Lengths on Inference of Chromosome Number Evolution in American Daisies (Melampodium, Asteraceae).

Science.gov (United States)

McCann, Jamie; Schneeweiss, Gerald M; Stuessy, Tod F; Villaseñor, Jose L; Weiss-Schneeweiss, Hanna

2016-01-01

Chromosome number change (polyploidy and dysploidy) plays an important role in plant diversification and speciation. Investigating chromosome number evolution commonly entails ancestral state reconstruction performed within a phylogenetic framework, which is, however, prone to uncertainty, whose effects on evolutionary inferences are insufficiently understood. Using the chromosomally diverse plant genus Melampodium (Asteraceae) as model group, we assess the impact of reconstruction method (maximum parsimony, maximum likelihood, Bayesian methods), branch length model (phylograms versus chronograms) and phylogenetic uncertainty (topological and branch length uncertainty) on the inference of chromosome number evolution. We also address the suitability of the maximum clade credibility (MCC) tree as single representative topology for chromosome number reconstruction. Each of the listed factors causes considerable incongruence among chromosome number reconstructions. Discrepancies between inferences on the MCC tree from those made by integrating over a set of trees are moderate for ancestral chromosome numbers, but severe for the difference of chromosome gains and losses, a measure of the directionality of dysploidy. Therefore, reliance on single trees, such as the MCC tree, is strongly discouraged and model averaging, taking both phylogenetic and model uncertainty into account, is recommended. For studying chromosome number evolution, dedicated models implemented in the program ChromEvol and ordered maximum parsimony may be most appropriate. Chromosome number evolution in Melampodium follows a pattern of bidirectional dysploidy (starting from x = 11 to x = 9 and x = 14, respectively) with no prevailing direction.

The Impact of Reconstruction Methods, Phylogenetic Uncertainty and Branch Lengths on Inference of Chromosome Number Evolution in American Daisies (Melampodium, Asteraceae.

Directory of Open Access Journals (Sweden)

Jamie McCann

Full Text Available Chromosome number change (polyploidy and dysploidy plays an important role in plant diversification and speciation. Investigating chromosome number evolution commonly entails ancestral state reconstruction performed within a phylogenetic framework, which is, however, prone to uncertainty, whose effects on evolutionary inferences are insufficiently understood. Using the chromosomally diverse plant genus Melampodium (Asteraceae as model group, we assess the impact of reconstruction method (maximum parsimony, maximum likelihood, Bayesian methods, branch length model (phylograms versus chronograms and phylogenetic uncertainty (topological and branch length uncertainty on the inference of chromosome number evolution. We also address the suitability of the maximum clade credibility (MCC tree as single representative topology for chromosome number reconstruction. Each of the listed factors causes considerable incongruence among chromosome number reconstructions. Discrepancies between inferences on the MCC tree from those made by integrating over a set of trees are moderate for ancestral chromosome numbers, but severe for the difference of chromosome gains and losses, a measure of the directionality of dysploidy. Therefore, reliance on single trees, such as the MCC tree, is strongly discouraged and model averaging, taking both phylogenetic and model uncertainty into account, is recommended. For studying chromosome number evolution, dedicated models implemented in the program ChromEvol and ordered maximum parsimony may be most appropriate. Chromosome number evolution in Melampodium follows a pattern of bidirectional dysploidy (starting from x = 11 to x = 9 and x = 14, respectively with no prevailing direction.

SEAPODYM-LTL: a parsimonious zooplankton dynamic biomass model

Science.gov (United States)

Conchon, Anna; Lehodey, Patrick; Gehlen, Marion; Titaud, Olivier; Senina, Inna; Séférian, Roland

2017-04-01

Mesozooplankton organisms are of critical importance for the understanding of early life history of most fish stocks, as well as the nutrient cycles in the ocean. Ongoing climate change and the need for improved approaches to the management of living marine resources has driven recent advances in zooplankton modelling. The classical modeling approach tends to describe the whole biogeochemical and plankton cycle with increasing complexity. We propose here a different and parsimonious zooplankton dynamic biomass model (SEAPODYM-LTL) that is cost efficient and can be advantageously coupled with primary production estimated either from satellite derived ocean color data or biogeochemical models. In addition, the adjoint code of the model is developed allowing a robust optimization approach for estimating the few parameters of the model. In this study, we run the first optimization experiments using a global database of climatological zooplankton biomass data and we make a comparative analysis to assess the importance of resolution and primary production inputs on model fit to observations. We also compare SEAPODYM-LTL outputs to those produced by a more complex biogeochemical model (PISCES) but sharing the same physical forcings.

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...

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...

UTILIZAÇÃO DE DIFERENTES ESTRUTURAS DE VARIÂNCIA RESIDUAL EM MODELOS DE REGRESSÃO ALEATÓRIA PARA DESCRIÇÃO DA CURVA DE CRESCIMENTO DE PERDIZES (Rhynchotus rufescens CRIADAS EM CATIVEIRO

Directory of Open Access Journals (Sweden)

Patrícia Tholon

2008-01-01

Full Text Available Random regression models (RRM allows considering heterogeneous residual variances to describe the growth for each age. However, this feature increases the number of parameters to be estimated in the maximization likelihood function process. Searching for more parsimonious RRM, several approaches have been suggested. One of them is the use of different structures of residual variances modelled through step function in different classes with similar variance or through variance functions. A total of 7,369 records of body weight of partridges, measured from birth to 210 days of partridges born from 2000 to 2004 were used in this research. The random regression models applied to the data set considered different structures of residual variances and were performed by the restricted maximum likelihood method. The residual variances were modeled using classes of 210 (R210 and 30 (R30 ages and variance functions with orders ranging from quadratic (VF2 to nine (VF9. The R30 considered birds weighted in the same week. The random effects included were the genetic additive direct and the permanent environment effects of the animal. It was not possible to include the maternal effects in the models. All random effects were modelled by sixth order regression on Legendre polynomials. The models were compared by the likelihood ratio test, the Akaike's information criterion and the Schwarz's Bayesian information criterion. Best results were showed by the models R210 and VF5. In conclusion, the most parsimonious model was VF5 and should be applied to fit growth records of partridges.

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

Science.gov (United States)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Molecular phylogenetics of emydine turtles: taxonomic revision and the evolution of shell kinesis.

Science.gov (United States)

Feldman, Chris R; Parham, James Ford

2002-03-01

The 10 extant species of emydine turtles represent an array of morphological and ecological forms recognizable and popular among scientists and hobbyists. Nevertheless, the phylogenetic affinities of most emydines remain contentious. Here, we examine the evolutionary relationships of emydine turtles using 2092 bp of DNA encoding the mitochondrial genes cyt b, ND4, and adjacent tRNAs. These data contain 339 parsimony informative characters that we use to erect hypotheses of relationships for the Emydinae. Both maximum parsimony and maximum likelihood methods yield a monophyletic Emydinae in which all but three nodes are well resolved. Emys orbicularis, Emydoidea blandingii, and Clemmys marmorata form a monophyletic clade, as do the species of Terrapene. Clemmys muhlenbergii and Clemmys insculpta form a third monophyletic group that may be sister to all other emydines. Clemmys guttata is problematic and probably related to Terrapene. Based on this phylogeny, and previous molecular work on the group, we suggest the following taxonomic revisions: (1) Clemmys should be restricted to a single species, C. guttata. (2) Calemys should be resurrected for C. muhlenbergii and C. insculpta. (3) Emys should be expanded to include three species: E. orbicularis, E. blandingii, and E. marmorata. Furthermore, our analyses show that neither kinetic-shelled nor akinetic-shelled emydines form monophyletic groups. Therefore, shell kinesis was either independently gained in Emys and Terrapene or secondarily lost in E. marmorata and C. guttata. Parsimony, paleontological evidence, and the multiple origins of shell kinesis in related turtle lineages (especially geoemydines) support the independent origin of plastral kinesis.

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

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

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.

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(')

Phylogenetic systematics of Barn Owl (Tyto alba (Scopoli, 1769 complex inferred from mitochondrial rDNA (16S rRNA taxonomic implication

Directory of Open Access Journals (Sweden)

Mansour Aliabadian

2012-09-01

Full Text Available The Barn owl, Tyto alba (Scopoli, 1769, occurs worldwide and shows a considerable amount of morphological and geographical variations, leading to the recognition of many subspecies throughout the world. Yet, no comprehensive study has not been done on this species. Data from mitochondrial gene (16S Ribosomal RNA (16S with 569 bp length were analyzed for 41 individuals around the world. Maximum likelihood (ML, maximum parsimony (MP and Bayesian analysis showed two distinct clades including alba clad (old world and furcata clad (new world. The amount of genetic variation within each of these clades ranged from 0.5-1.7 but variation between clades was 3.7. This data may suggest that Barn owls of the Old World may be a separate species from those of the New World.

The tempo and mode of barnacle evolution

DEFF Research Database (Denmark)

Pérez-Losada, Marcos; Harp, Margaret; Høeg, Jens T

2008-01-01

(outgroup) species representing almost all the Thoracica families to assess the tempo and mode of barnacle evolution. Using phylogenetic methods of maximum parsimony, maximum likelihood, and Bayesian inference and 14 fossil calibrations, we found that: (1) Iblomorpha form a monophyletic group; (2......) pedunculated barnacles without shell plates (Heteralepadomorpha) are not ancestral, but have evolved, at least twice, from plated forms; (3) the ontogenetic pattern with 5-->6-->8-->12+ plates does not reflect Thoracica shell evolution; (4) the traditional asymmetric barnacles (Verrucidae) and the Balanomorpha......) the Thoracica suborders evolved since the Early Carboniferous (340mya) with the final radiation of the Sessilia in the Upper Jurassic (147mya). These results, therefore, reject many of the underlying hypotheses about character evolution in the Cirripedia Thoracica, stimulate a variety of new thoughts...

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.

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 ...

A Practical pedestrian approach to parsimonious regression with inaccurate inputs

Directory of Open Access Journals (Sweden)

Seppo Karrila

2014-04-01

Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.

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

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.)

Time-Lapse Monitoring of Subsurface Fluid Flow using Parsimonious Seismic Interferometry

KAUST Repository

Hanafy, Sherif

2017-04-21

A typical small-scale seismic survey (such as 240 shot gathers) takes at least 16 working hours to be completed, which is a major obstacle in case of time-lapse monitoring experiments. This is especially true if the subject that needs to be monitored is rapidly changing. In this work, we will discuss how to decrease the recording time from 16 working hours to less than one hour of recording. Here, the virtual data has the same accuracy as the conventional data. We validate the efficacy of parsimonious seismic interferometry with the time-lapse mentoring idea with field examples, where we were able to record 30 different data sets within a 2-hour period. The recorded data are then processed to generate 30 snapshots that shows the spread of water from the ground surface down to a few meters.

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

Stochastic conditional intensity processes

DEFF Research Database (Denmark)

Bauwens, Luc; Hautsch, Nikolaus

2006-01-01

model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence......In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell’s (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...

Composite scores in comparative effectiveness research: counterbalancing parsimony and dimensionality in patient-reported outcomes.

Science.gov (United States)

Schwartz, Carolyn E; Patrick, Donald L

2014-07-01

When planning a comparative effectiveness study comparing disease-modifying treatments, competing demands influence choice of outcomes. Current practice emphasizes parsimony, although understanding multidimensional treatment impact can help to personalize medical decision-making. We discuss both sides of this 'tug of war'. We discuss the assumptions, advantages and drawbacks of composite scores and multidimensional outcomes. We describe possible solutions to the multiple comparison problem, including conceptual hierarchy distinctions, statistical approaches, 'real-world' benchmarks of effectiveness and subgroup analysis. We conclude that comparative effectiveness research should consider multiple outcome dimensions and compare different approaches that fit the individual context of study objectives.

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.

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.

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.

Philosophical conjectures and their refutation.

Science.gov (United States)

Kluge, A G

2001-06-01

Sir Karl Popper is well known for explicating science in falsificationist terms, for which his degree of corroboration formalism, C(h,e,b), has become little more than a symbol. For example, de Queiroz and Poe in this issue argue that C(h,e,b) reduces to a single relative (conditional) probability, p(e,hb), the likelihood of evidence e, given both hypothesis h and background knowledge b, and in reaching that conclusion, without stating or expressing it, they render Popper a verificationist. The contradiction they impose is easily explained--de Queiroz and Poe fail to take account of the fact that Popper derived C(h,e,b) from absolute (logical) probability and severity of test, S(e,h,b), where critical evidence, p(e,b), is fundamental. Thus, de Queiroz and Poe's conjecture that p(e,hb) = C(h,e,b) is refuted. Falsificationism, not verificationism, remains a fair description of the parsimony method of inference used in phylogenetic systematics, not withstanding de Queiroz and Poe's mistaken understanding that "statistical" probability justifies that method. Although de Queiroz and Poe assert that maximum likelihood has the power "to explain data", they do not successfully demonstrate how causal explanation is achieved or what it is that is being explained. This is not surprising, bearing in mind that what is assumed about character evolution in the accompanying likelihood model M cannot then be explained by the results of a maximum likelihood analysis.

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.

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.

The Laplace Likelihood Ratio Test for Heteroscedasticity

Directory of Open Access Journals (Sweden)

J. Martin van Zyl

2011-01-01

Full Text Available It is shown that the likelihood ratio test for heteroscedasticity, assuming the Laplace distribution, gives good results for Gaussian and fat-tailed data. The likelihood ratio test, assuming normality, is very sensitive to any deviation from normality, especially when the observations are from a distribution with fat tails. Such a likelihood test can also be used as a robust test for a constant variance in residuals or a time series if the data is partitioned into groups.

Comprehensive Phylogenetic Analysis of Bovine Non-aureus Staphylococci Species Based on Whole-Genome Sequencing

Science.gov (United States)

Naushad, Sohail; Barkema, Herman W.; Luby, Christopher; Condas, Larissa A. Z.; Nobrega, Diego B.; Carson, Domonique A.; De Buck, Jeroen

2016-01-01

Non-aureus staphylococci (NAS), a heterogeneous group of a large number of species and subspecies, are the most frequently isolated pathogens from intramammary infections in dairy cattle. Phylogenetic relationships among bovine NAS species are controversial and have mostly been determined based on single-gene trees. Herein, we analyzed phylogeny of bovine NAS species using whole-genome sequencing (WGS) of 441 distinct isolates. In addition, evolutionary relationships among bovine NAS were estimated from multilocus data of 16S rRNA, hsp60, rpoB, sodA, and tuf genes and sequences from these and numerous other single genes/proteins. All phylogenies were created with FastTree, Maximum-Likelihood, Maximum-Parsimony, and Neighbor-Joining methods. Regardless of methodology, WGS-trees clearly separated bovine NAS species into five monophyletic coherent clades. Furthermore, there were consistent interspecies relationships within clades in all WGS phylogenetic reconstructions. Except for the Maximum-Parsimony tree, multilocus data analysis similarly produced five clades. There were large variations in determining clades and interspecies relationships in single gene/protein trees, under different methods of tree constructions, highlighting limitations of using single genes for determining bovine NAS phylogeny. However, based on WGS data, we established a robust phylogeny of bovine NAS species, unaffected by method or model of evolutionary reconstructions. Therefore, it is now possible to determine associations between phylogeny and many biological traits, such as virulence, antimicrobial resistance, environmental niche, geographical distribution, and host specificity. PMID:28066335

Pengintegrasian Model Leadership Menuju Model yang Lebih Komprhensip dan Parsimoni

Directory of Open Access Journals (Sweden)

Miswanto Miswanti

2016-06-01

Full Text Available ABTSRACT Through leadership models offered by Locke et. al (1991 we can say that whether good or not the vision of leaders in the organization is highly dependent on whether good or not the motives and traits, knowledge, skill, and abilities owned leaders. Then, good or not the implementation of the vision by the leader depends on whether good or not the motives and traits, knowledge, skills, abilities, and the vision of the leaders. Strategic Leadership written by Davies (1991 states that the implementation of the vision by using strategic leadership, the meaning is much more complete than what has been written by Locke et. al. in the fourth stage of leadership. Thus, aspects of the implementation of the vision by Locke et al (1991 it is not complete implementation of the vision according to Davies (1991. With the considerations mentioned above, this article attempts to combine the leadership model of the Locke et. al and strategic leadership of the Davies. With this modification is expected to be an improvement model of leadership is more comprehensive and parsimony.

Phylogenetic Analysis of a 'Jewel Orchid' Genus Goodyera (Orchidaceae) Based on DNA Sequence Data from Nuclear and Plastid Regions.

Science.gov (United States)

Hu, Chao; Tian, Huaizhen; Li, Hongqing; Hu, Aiqun; Xing, Fuwu; Bhattacharjee, Avishek; Hsu, Tianchuan; Kumar, Pankaj; Chung, Shihwen

2016-01-01

A molecular phylogeny of Asiatic species of Goodyera (Orchidaceae, Cranichideae, Goodyerinae) based on the nuclear ribosomal internal transcribed spacer (ITS) region and two chloroplast loci (matK and trnL-F) was presented. Thirty-five species represented by 132 samples of Goodyera were analyzed, along with other 27 genera/48 species, using Pterostylis longifolia and Chloraea gaudichaudii as outgroups. Bayesian inference, maximum parsimony and maximum likelihood methods were used to reveal the intrageneric relationships of Goodyera and its intergeneric relationships to related genera. The results indicate that: 1) Goodyera is not monophyletic; 2) Goodyera could be divided into four sections, viz., Goodyera, Otosepalum, Reticulum and a new section; 3) sect. Reticulum can be further divided into two subsections, viz., Reticulum and Foliosum, whereas sect. Goodyera can in turn be divided into subsections Goodyera and a new subsection.

Phylogenetic Analysis of a 'Jewel Orchid' Genus Goodyera (Orchidaceae Based on DNA Sequence Data from Nuclear and Plastid Regions.

Directory of Open Access Journals (Sweden)

Chao Hu

Full Text Available A molecular phylogeny of Asiatic species of Goodyera (Orchidaceae, Cranichideae, Goodyerinae based on the nuclear ribosomal internal transcribed spacer (ITS region and two chloroplast loci (matK and trnL-F was presented. Thirty-five species represented by 132 samples of Goodyera were analyzed, along with other 27 genera/48 species, using Pterostylis longifolia and Chloraea gaudichaudii as outgroups. Bayesian inference, maximum parsimony and maximum likelihood methods were used to reveal the intrageneric relationships of Goodyera and its intergeneric relationships to related genera. The results indicate that: 1 Goodyera is not monophyletic; 2 Goodyera could be divided into four sections, viz., Goodyera, Otosepalum, Reticulum and a new section; 3 sect. Reticulum can be further divided into two subsections, viz., Reticulum and Foliosum, whereas sect. Goodyera can in turn be divided into subsections Goodyera and a new subsection.

Phylogenetic Analysis of a ‘Jewel Orchid’ Genus Goodyera (Orchidaceae) Based on DNA Sequence Data from Nuclear and Plastid Regions

Science.gov (United States)

Hu, Chao; Tian, Huaizhen; Li, Hongqing; Hu, Aiqun; Xing, Fuwu; Bhattacharjee, Avishek; Hsu, Tianchuan; Kumar, Pankaj; Chung, Shihwen

2016-01-01

A molecular phylogeny of Asiatic species of Goodyera (Orchidaceae, Cranichideae, Goodyerinae) based on the nuclear ribosomal internal transcribed spacer (ITS) region and two chloroplast loci (matK and trnL-F) was presented. Thirty-five species represented by 132 samples of Goodyera were analyzed, along with other 27 genera/48 species, using Pterostylis longifolia and Chloraea gaudichaudii as outgroups. Bayesian inference, maximum parsimony and maximum likelihood methods were used to reveal the intrageneric relationships of Goodyera and its intergeneric relationships to related genera. The results indicate that: 1) Goodyera is not monophyletic; 2) Goodyera could be divided into four sections, viz., Goodyera, Otosepalum, Reticulum and a new section; 3) sect. Reticulum can be further divided into two subsections, viz., Reticulum and Foliosum, whereas sect. Goodyera can in turn be divided into subsections Goodyera and a new subsection. PMID:26927946

REGEN: Ancestral Genome Reconstruction for Bacteria.

Science.gov (United States)

Yang, Kuan; Heath, Lenwood S; Setubal, João C

2012-07-18

Ancestral genome reconstruction can be understood as a phylogenetic study with more details than a traditional phylogenetic tree reconstruction. We present a new computational system called REGEN for ancestral bacterial genome reconstruction at both the gene and replicon levels. REGEN reconstructs gene content, contiguous gene runs, and replicon structure for each ancestral genome. Along each branch of the phylogenetic tree, REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order. We evaluated REGEN using simulated genomes and genomes in the Rhizobiales order.

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.

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.

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....

MRL and SuperFine+MRL: new supertree methods

Science.gov (United States)

2012-01-01

Background Supertree methods combine trees on subsets of the full taxon set together to produce a tree on the entire set of taxa. Of the many supertree methods, the most popular is MRP (Matrix Representation with Parsimony), a method that operates by first encoding the input set of source trees by a large matrix (the "MRP matrix") over {0,1, ?}, and then running maximum parsimony heuristics on the MRP matrix. Experimental studies evaluating MRP in comparison to other supertree methods have established that for large datasets, MRP generally produces trees of equal or greater accuracy than other methods, and can run on larger datasets. A recent development in supertree methods is SuperFine+MRP, a method that combines MRP with a divide-and-conquer approach, and produces more accurate trees in less time than MRP. In this paper we consider a new approach for supertree estimation, called MRL (Matrix Representation with Likelihood). MRL begins with the same MRP matrix, but then analyzes the MRP matrix using heuristics (such as RAxML) for 2-state Maximum Likelihood. Results We compared MRP and SuperFine+MRP with MRL and SuperFine+MRL on simulated and biological datasets. We examined the MRP and MRL scores of each method on a wide range of datasets, as well as the resulting topological accuracy of the trees. Our experimental results show that MRL, coupled with a very good ML heuristic such as RAxML, produced more accurate trees than MRP, and MRL scores were more strongly correlated with topological accuracy than MRP scores. Conclusions SuperFine+MRP, when based upon a good MP heuristic, such as TNT, produces among the best scores for both MRP and MRL, and is generally faster and more topologically accurate than other supertree methods we tested. PMID:22280525

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.

Choosing the best ancestral character state reconstruction method.

Science.gov (United States)

Royer-Carenzi, Manuela; Pontarotti, Pierre; Didier, Gilles

2013-03-01

Despite its intrinsic difficulty, ancestral character state reconstruction is an essential tool for testing evolutionary hypothesis. Two major classes of approaches to this question can be distinguished: parsimony- or likelihood-based approaches. We focus here on the second class of methods, more specifically on approaches based on continuous-time Markov modeling of character evolution. Among them, we consider the most-likely-ancestor reconstruction, the posterior-probability reconstruction, the likelihood-ratio method, and the Bayesian approach. We discuss and compare the above-mentioned methods over several phylogenetic trees, adding the maximum-parsimony method performance in the comparison. Under the assumption that the character evolves according a continuous-time Markov process, we compute and compare the expectations of success of each method for a broad range of model parameter values. Moreover, we show how the knowledge of the evolution model parameters allows to compute upper bounds of reconstruction performances, which are provided as references. The results of all these reconstruction methods are quite close one to another, and the expectations of success are not so far from their theoretical upper bounds. But the performance ranking heavily depends on the topology of the studied tree, on the ancestral node that is to be inferred and on the parameter values. Consequently, we propose a protocol providing for each parameter value the best method in terms of expectation of success, with regard to the phylogenetic tree and the ancestral node to infer. Copyright © 2012 Elsevier Inc. All rights reserved.

Posterior distributions for likelihood ratios in forensic science.

Science.gov (United States)

van den Hout, Ardo; Alberink, Ivo

2016-09-01

Evaluation of evidence in forensic science is discussed using posterior distributions for likelihood ratios. Instead of eliminating the uncertainty by integrating (Bayes factor) or by conditioning on parameter values, uncertainty in the likelihood ratio is retained by parameter uncertainty derived from posterior distributions. A posterior distribution for a likelihood ratio can be summarised by the median and credible intervals. Using the posterior mean of the distribution is not recommended. An analysis of forensic data for body height estimation is undertaken. The posterior likelihood approach has been criticised both theoretically and with respect to applicability. This paper addresses the latter and illustrates an interesting application area. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Singular Spectrum Analysis for Astronomical Time Series: Constructing a Parsimonious Hypothesis Test

Science.gov (United States)

Greco, G.; Kondrashov, D.; Kobayashi, S.; Ghil, M.; Branchesi, M.; Guidorzi, C.; Stratta, G.; Ciszak, M.; Marino, F.; Ortolan, A.

We present a data-adaptive spectral method - Monte Carlo Singular Spectrum Analysis (MC-SSA) - and its modification to tackle astrophysical problems. Through numerical simulations we show the ability of the MC-SSA in dealing with 1/f β power-law noise affected by photon counting statistics. Such noise process is simulated by a first-order autoregressive, AR(1) process corrupted by intrinsic Poisson noise. In doing so, we statistically estimate a basic stochastic variation of the source and the corresponding fluctuations due to the quantum nature of light. In addition, MC-SSA test retains its effectiveness even when a significant percentage of the signal falls below a certain level of detection, e.g., caused by the instrument sensitivity. The parsimonious approach presented here may be broadly applied, from the search for extrasolar planets to the extraction of low-intensity coherent phenomena probably hidden in high energy transients.

On the Accuracy of Ancestral Sequence Reconstruction for Ultrametric Trees with Parsimony.

Science.gov (United States)

Herbst, Lina; Fischer, Mareike

2018-04-01

We examine a mathematical question concerning the reconstruction accuracy of the Fitch algorithm for reconstructing the ancestral sequence of the most recent common ancestor given a phylogenetic tree and sequence data for all taxa under consideration. In particular, for the symmetric four-state substitution model which is also known as Jukes-Cantor model, we answer affirmatively a conjecture of Li, Steel and Zhang which states that for any ultrametric phylogenetic tree and a symmetric model, the Fitch parsimony method using all terminal taxa is more accurate, or at least as accurate, for ancestral state reconstruction than using any particular terminal taxon or any particular pair of taxa. This conjecture had so far only been answered for two-state data by Fischer and Thatte. Here, we focus on answering the biologically more relevant case with four states, which corresponds to ancestral sequence reconstruction from DNA or RNA data.

Likelihood analysis of parity violation in the compound nucleus

International Nuclear Information System (INIS)

Bowman, D.; Sharapov, E.

1993-01-01

We discuss the determination of the root mean-squared matrix element of the parity-violating interaction between compound-nuclear states using likelihood analysis. We briefly review the relevant features of the statistical model of the compound nucleus and the formalism of likelihood analysis. We then discuss the application of likelihood analysis to data on panty-violating longitudinal asymmetries. The reliability of the extracted value of the matrix element and errors assigned to the matrix element is stressed. We treat the situations where the spins of the p-wave resonances are not known and known using experimental data and Monte Carlo techniques. We conclude that likelihood analysis provides a reliable way to determine M and its confidence interval. We briefly discuss some problems associated with the normalization of the likelihood function

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.

Perils of parsimony: properties of reduced-rank estimates of genetic covariance matrices.

Science.gov (United States)

Meyer, Karin; Kirkpatrick, Mark

2008-10-01

Eigenvalues and eigenvectors of covariance matrices are important statistics for multivariate problems in many applications, including quantitative genetics. Estimates of these quantities are subject to different types of bias. This article reviews and extends the existing theory on these biases, considering a balanced one-way classification and restricted maximum-likelihood estimation. Biases are due to the spread of sample roots and arise from ignoring selected principal components when imposing constraints on the parameter space, to ensure positive semidefinite estimates or to estimate covariance matrices of chosen, reduced rank. In addition, it is shown that reduced-rank estimators that consider only the leading eigenvalues and -vectors of the "between-group" covariance matrix may be biased due to selecting the wrong subset of principal components. In a genetic context, with groups representing families, this bias is inverse proportional to the degree of genetic relationship among family members, but is independent of sample size. Theoretical results are supplemented by a simulation study, demonstrating close agreement between predicted and observed bias for large samples. It is emphasized that the rank of the genetic covariance matrix should be chosen sufficiently large to accommodate all important genetic principal components, even though, paradoxically, this may require including a number of components with negligible eigenvalues. A strategy for rank selection in practical analyses is outlined.

Patterns and effects of GC3 heterogeneity and parsimony informative sites on the phylogenetic tree of genes.

Science.gov (United States)

Ma, Shuai; Wu, Qi; Hu, Yibo; Wei, Fuwen

2018-05-20

The explosive growth in genomic data has provided novel insights into the conflicting signals hidden in phylogenetic trees. Although some studies have explored the effects of the GC content and parsimony informative sites (PIS) on the phylogenetic tree, the effect of the heterogeneity of the GC content at the first/second/third codon position on parsimony informative sites (GC1/2/3 PIS ) among different species and the effect of PIS on phylogenetic tree construction remain largely unexplored. Here, we used two different mammal genomic datasets to explore the patterns of GC1/2/3 PIS heterogeneity and the effect of PIS on the phylogenetic tree of genes: (i) all GC1/2/3 PIS have obvious heterogeneity between different mammals, and the levels of heterogeneity are GC3 PIS  > GC2 PIS  > GC1 PIS ; (ii) the number of PIS is positively correlated with the metrics of "good" gene tree topologies, and excluding the third codon position (C3) decreases the quality of gene trees by removing too many PIS. These results provide novel insights into the heterogeneity pattern of GC1/2/3 PIS in mammals and the relationship between GC3/PIS and gene trees. Additionally, it is necessary to carefully consider whether to exclude C3 to improve the quality of gene trees, especially in the super-tree method. Copyright © 2018 Elsevier B.V. All rights reserved.

Invariant Versus Classical Quartet Inference When Evolution is Heterogeneous Across Sites and Lineages.

Science.gov (United States)

Fernández-Sánchez, Jesús; Casanellas, Marta

2016-03-01

One reason why classical phylogenetic reconstruction methods fail to correctly infer the underlying topology is because they assume oversimplified models. In this article, we propose a quartet reconstruction method consistent with the most general Markov model of nucleotide substitution, which can also deal with data coming from mixtures on the same topology. Our proposed method uses phylogenetic invariants and provides a system of weights that can be used as input for quartet-based methods. We study its performance on real data and on a wide range of simulated 4-taxon data (both time-homogeneous and nonhomogeneous, with or without among-site rate heterogeneity, and with different branch length settings). We compare it to the classical methods of neighbor-joining (with paralinear distance), maximum likelihood (with different underlying models), and maximum parsimony. Our results show that this method is accurate and robust, has a similar performance to maximum likelihood when data satisfies the assumptions of both methods, and outperform the other methods when these are based on inappropriate substitution models. If alignments are long enough, then it also outperforms other methods when some of its assumptions are violated. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Balancing practicality and hydrologic realism: a parsimonious approach for simulating rapid groundwater recharge via unsaturated-zone preferential flow

Science.gov (United States)

Mirus, Benjamin B.; Nimmo, J.R.

2013-01-01

The impact of preferential flow on recharge and contaminant transport poses a considerable challenge to water-resources management. Typical hydrologic models require extensive site characterization, but can underestimate fluxes when preferential flow is significant. A recently developed source-responsive model incorporates film-flow theory with conservation of mass to estimate unsaturated-zone preferential fluxes with readily available data. The term source-responsive describes the sensitivity of preferential flow in response to water availability at the source of input. We present the first rigorous tests of a parsimonious formulation for simulating water table fluctuations using two case studies, both in arid regions with thick unsaturated zones of fractured volcanic rock. Diffuse flow theory cannot adequately capture the observed water table responses at both sites; the source-responsive model is a viable alternative. We treat the active area fraction of preferential flow paths as a scaled function of water inputs at the land surface then calibrate the macropore density to fit observed water table rises. Unlike previous applications, we allow the characteristic film-flow velocity to vary, reflecting the lag time between source and deep water table responses. Analysis of model performance and parameter sensitivity for the two case studies underscores the importance of identifying thresholds for initiation of film flow in unsaturated rocks, and suggests that this parsimonious approach is potentially of great practical value.

Maintaining symmetry of simulated likelihood functions

DEFF Research Database (Denmark)

Andersen, Laura Mørch

This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby...... improves precision substantially. Another source of error is that models testing away mixing dimensions must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. These simulation errors are ignored in the standard estimation procedures used today...

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.

REGEN: Ancestral Genome Reconstruction for Bacteria

Directory of Open Access Journals (Sweden)

João C. Setubal

2012-07-01

Full Text Available Ancestral genome reconstruction can be understood as a phylogenetic study with more details than a traditional phylogenetic tree reconstruction. We present a new computational system called REGEN for ancestral bacterial genome reconstruction at both the gene and replicon levels. REGEN reconstructs gene content, contiguous gene runs, and replicon structure for each ancestral genome. Along each branch of the phylogenetic tree, REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order. We evaluated REGEN using simulated genomes and genomes in the Rhizobiales order.

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies.

Science.gov (United States)

Min, Xiaoping; Zhang, Mouzhao; Yuan, Sisi; Ge, Shengxiang; Liu, Xiangrong; Zeng, Xiangxiang; Xia, Ningshao

2017-12-26

In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.

Molecular characterization and phylogeny of some mazocraeidean monogeneans from carangid fish.

Science.gov (United States)

Tambireddy, Neeraja; Gayatri, Tripathi; Gireesh-Babu, Pathakota; Pavan-Kumar, Annam

2016-03-01

Polyopisthocotylean monogenean parasites of fishes are highly host specific and have been used as an appropriate model to study the host-parasite co-evolution. In the present study, eight monogeneans of the order Mazocraeidea were characterized by nuclear 28S rDNA sequences and their phylogenetic relationship with other polyopisthocotylean species was investigated. Neighbour-joining, maximum parsimony, maximum likelihood and Bayesian Inference methods were used for phylogenetic reconstruction. The topology sustained by high bootstrap was: (((Hexabothriidae (Mazocraeidae (Discocotylidae (Diplozoidae (Diclidophoridae (Plectanocotylidae (Heteromicrocotylidae (Microcotylidae (Heteraxinidae), (Thoracocotylidae, Gotocotylidae (Gastrocoylidae (Allodiscocotylidae: Protomicrocotylidae))). In addition, we have also developed DNA barcodes (COI sequences) for six species and the barcodes clearly discriminated all the species. The polytomy within Protomicrocotylidae family is resolved in this study for the first time and it appears that within this family, Bilaterocotyloides species are basal compared to Neomicrocotyle and Lethacotyle species while the latter is the more derived.

Effects of methodology and analysis strategy on robustness of pestivirus phylogeny.

Science.gov (United States)

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

2010-01-01

Phylogenetic analysis of pestiviruses is a useful tool for classifying novel pestiviruses and for revealing their phylogenetic relationships. In this study, robustness of pestivirus phylogenies has been compared by analyses of the 5'UTR, and complete N(pro) and E2 gene regions separately and combined, performed by four methods: neighbour-joining (NJ), maximum parsimony (MP), maximum likelihood (ML), and Bayesian inference (BI). The strategy of analysing the combined sequence dataset by BI, ML, and MP methods resulted in a single, well-supported tree topology, indicating a reliable and robust pestivirus phylogeny. By contrast, the single-gene analysis strategy resulted in 12 trees of different topologies, revealing different relationships among pestiviruses. These results indicate that the strategies and methodologies are two vital aspects affecting the robustness of the pestivirus phylogeny. The strategy and methodologies outlined in this paper may have a broader application in inferring phylogeny of other RNA viruses.

Analysis of Acorus calamus chloroplast genome and its phylogenetic implications.

Science.gov (United States)

Goremykin, Vadim V; Holland, Barbara; Hirsch-Ernst, Karen I; Hellwig, Frank H

2005-09-01

Determining the phylogenetic relationships among the major lines of angiosperms is a long-standing problem, yet the uncertainty as to the phylogenetic affinity of these lines persists. While a number of studies have suggested that the ANITA (Amborella-Nymphaeales-Illiciales-Trimeniales-Aristolochiales) grade is basal within angiosperms, studies of complete chloroplast genome sequences also suggested an alternative tree, wherein the line leading to the grasses branches first among the angiosperms. To improve taxon sampling in the existing chloroplast genome data, we sequenced the chloroplast genome of the monocot Acorus calamus. We generated a concatenated alignment (89,436 positions for 15 taxa), encompassing almost all sequences usable for phylogeny reconstruction within spermatophytes. The data still contain support for both the ANITA-basal and grasses-basal hypotheses. Using simulations we can show that were the ANITA-basal hypothesis true, parsimony (and distance-based methods with many models) would be expected to fail to recover it. The self-evident explanation for this failure appears to be a long-branch attraction (LBA) between the clade of grasses and the out-group. However, this LBA cannot explain the discrepancies observed between tree topology recovered using the maximum likelihood (ML) method and the topologies recovered using the parsimony and distance-based methods when grasses are deleted. Furthermore, the fact that neither maximum parsimony nor distance methods consistently recover the ML tree, when according to the simulations they would be expected to, when the out-group (Pinus) is deleted, suggests that either the generating tree is not correct or the best symmetric model is misspecified (or both). We demonstrate that the tree recovered under ML is extremely sensitive to model specification and that the best symmetric model is misspecified. Hence, we remain agnostic regarding phylogenetic relationships among basal angiosperm lineages.

More quality measures versus measuring what matters: a call for balance and parsimony.

Science.gov (United States)

Meyer, Gregg S; Nelson, Eugene C; Pryor, David B; James, Brent; Swensen, Stephen J; Kaplan, Gary S; Weissberg, Jed I; Bisognano, Maureen; Yates, Gary R; Hunt, Gordon C

2012-11-01

External groups requiring measures now include public and private payers, regulators, accreditors and others that certify performance levels for consumers, patients and payers. Although benefits have accrued from the growth in quality measurement, the recent explosion in the number of measures threatens to shift resources from improving quality to cover a plethora of quality-performance metrics that may have a limited impact on the things that patients and payers want and need (ie, better outcomes, better care, and lower per capita costs). Here we propose a policy that quality measurement should be: balanced to meet the need of end users to judge quality and cost performance and the need of providers to continuously improve the quality, outcomes and costs of their services; and parsimonious to measure quality, outcomes and costs with appropriate metrics that are selected based on end-user needs.

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.

The fine-tuning cost of the likelihood in SUSY models

CERN Document Server

Ghilencea, D M

2013-01-01

In SUSY models, the fine tuning of the electroweak (EW) scale with respect to their parameters gamma_i={m_0, m_{1/2}, mu_0, A_0, B_0,...} and the maximal likelihood L to fit the experimental data are usually regarded as two different problems. We show that, if one regards the EW minimum conditions as constraints that fix the EW scale, this commonly held view is not correct and that the likelihood contains all the information about fine-tuning. In this case we show that the corrected likelihood is equal to the ratio L/Delta of the usual likelihood L and the traditional fine tuning measure Delta of the EW scale. A similar result is obtained for the integrated likelihood over the set {gamma_i}, that can be written as a surface integral of the ratio L/Delta, with the surface in gamma_i space determined by the EW minimum constraints. As a result, a large likelihood actually demands a large ratio L/Delta or equivalently, a small chi^2_{new}=chi^2_{old}+2*ln(Delta). This shows the fine-tuning cost to the likelihood ...

Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes

Directory of Open Access Journals (Sweden)

Galperin Michael Y

2003-01-01

indicates that, even with a gain penalty of 1 (equal weights assigned to a gain and a loss, the set of 572 genes assigned to LUCA might be nearly sufficient to sustain a functioning organism. Under this gain penalty value, the numbers of horizontal gene transfer and gene loss events are nearly identical. This result holds true for two alternative topologies of the species tree and even under random shuffling of the tree. Therefore, the results seem to be compatible with approximately equal likelihoods of HGT and gene loss in the evolution of prokaryotes. Conclusions The notion that gene loss and HGT are major aspects of prokaryotic evolution was supported by quantitative analysis of the mapping of the phyletic patterns of COGs onto a hypothetical species tree. Algorithms were developed for constructing parsimonious evolutionary scenarios, which include gene loss and gain events, for orthologous gene sets, given a species tree. This analysis shows, contrary to expectations, that the number of predicted HGT events that occurred during the evolution of prokaryotes might be approximately the same as the number of gene losses. The approach to the reconstruction of evolutionary scenarios employed here is conservative with regard to the detection of HGT because only patterns of gene presence-absence in sequenced genomes are taken into account. In reality, horizontal transfer might have contributed to the evolution of many other genes also, which makes it a dominant force in prokaryotic evolution.

B-Spline potential function for maximum a-posteriori image reconstruction in fluorescence microscopy

Directory of Open Access Journals (Sweden)

Shilpa Dilipkumar

2015-03-01

Full Text Available An iterative image reconstruction technique employing B-Spline potential function in a Bayesian framework is proposed for fluorescence microscopy images. B-splines are piecewise polynomials with smooth transition, compact support and are the shortest polynomial splines. Incorporation of the B-spline potential function in the maximum-a-posteriori reconstruction technique resulted in improved contrast, enhanced resolution and substantial background reduction. The proposed technique is validated on simulated data as well as on the images acquired from fluorescence microscopes (widefield, confocal laser scanning fluorescence and super-resolution 4Pi microscopy. A comparative study of the proposed technique with the state-of-art maximum likelihood (ML and maximum-a-posteriori (MAP with quadratic potential function shows its superiority over the others. B-Spline MAP technique can find applications in several imaging modalities of fluorescence microscopy like selective plane illumination microscopy, localization microscopy and STED.

Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Directory of Open Access Journals (Sweden)

Rodrigo Reis Mota

2016-09-01

Full Text Available ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days in Simmental cattle using principal components, as well as to estimate (covariance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC and Bayesian information (BIC criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML. The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days to 0.30 (730 days. Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.

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.

High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

KAUST Repository

Castruccio, Stefano; Huser, Raphaë l; Genton, Marc G.

2015-01-01

In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

KAUST Repository

Castruccio, Stefano

2015-09-29

In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.