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Maximum Likelihood Estimation In The Random Coefficient Regression Model Via The Em Algorithm
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Book Synopsis Maximum Likelihood Estimation in the Random Coefficient Regression Model Via the EM Algorithm by : Jiang-Ming Wu
Download or read book Maximum Likelihood Estimation in the Random Coefficient Regression Model Via the EM Algorithm written by Jiang-Ming Wu and published by . This book was released on 1995 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation and Inference by : Russell B. Millar
Download or read book Maximum Likelihood Estimation and Inference written by Russell B. Millar and published by John Wiley & Sons. This book was released on 2011-07-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Book Synopsis Maximum Likelihood Sequence Estimation Via the Expectation Maximization Algorithm in the Presence of Random Phase and Amplitude Fading by : Jae Choong Han
Download or read book Maximum Likelihood Sequence Estimation Via the Expectation Maximization Algorithm in the Presence of Random Phase and Amplitude Fading written by Jae Choong Han and published by . This book was released on 1994 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Linear Regression Models with Random Coefficient by : K L. Krishna
Download or read book Maximum Likelihood Estimation of Linear Regression Models with Random Coefficient written by K L. Krishna and published by . This book was released on 1970 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Multilevel Analysis by : Jan Deleeuw
Download or read book Handbook of Multilevel Analysis written by Jan Deleeuw and published by Springer Science & Business Media. This book was released on 2007-12-26 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.
Book Synopsis Maximum Likelihood Estimation of the Truncated and Censored Normal Regression Models by : Michael J. Hartley
Download or read book Maximum Likelihood Estimation of the Truncated and Censored Normal Regression Models written by Michael J. Hartley and published by . This book was released on 1985 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimating the Covariance Components of an Unbalanced Multivariate Latent Random Model Via the EM Algorithm by : Leonard Joseph Bianchi
Download or read book Estimating the Covariance Components of an Unbalanced Multivariate Latent Random Model Via the EM Algorithm written by Leonard Joseph Bianchi and published by . This book was released on 1987 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm by : Antara Majumdar
Download or read book Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm written by Antara Majumdar and published by . This book was released on 2007 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Likelihood based estimation of stochastic models when one of the explanatory variables is masked by measurement error, is presented. Special methods are required to estimate the parameters of a model with one or more explanatory variables that are measured with error. In such models, the variable measured with error is unobservable. Only an unbiased manifestation is observable. The method proposed, provides an adjustment to obtain unbiased estimates of model parameters. The correction of bias, however, is not possible without additional identifying information. An instrumental variable is a practical form of additional information that can be used for this purpose. By treating the unobservable explanatory variable as 'missing' data the Markov Chain Monte Carlo Expectation Maximization (MCEM) algorithm is applied for maximum likelihood estimation of the parameters of a measurement error model with identifying information in the form of an instrumental variable. Implementation strategies, computational aspects, behavior of the estimators and inference resulting from application of the MCEM algorithm to the instrumental variable measurement error model are studied. A general methodology is developed that encompasses a variety of previously studied special case models and it is shown how they all can be modeled and estimated using the MCEM algorithm. Through our method it is shown how a structural logistic regression measurement error model can be directly fitted without the probit approximation. This was not possible prior to the research presented in this dissertation. The proposed methodology is compared numerically with the exact maximum likelihood estimates for two normal family models. Also, the behavior of the method is investigated when one of the variance parameters is near the boundary of the parameter space. The problem of measurement error in a survival time model with right censoring is considered and it is shown how the proposed method can be used to estimate a hazard function model, by construction of some special likelihoods and further methodological development. Two methods have been proposed, one of which is a semi-parametric method and the other is full parametric.
Book Synopsis Maximum Likelihood Estimation in Vector Long Memory Processes Via EM Algorithm by : Jeffrey Pai
Download or read book Maximum Likelihood Estimation in Vector Long Memory Processes Via EM Algorithm written by Jeffrey Pai and published by . This book was released on 2008 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of VARMA Models Using a State-Space EM Algorithm by : Konstantinos Metaxoglou
Download or read book Maximum Likelihood Estimation of VARMA Models Using a State-Space EM Algorithm written by Konstantinos Metaxoglou and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a state-space representation for vector autoregressive moving-average models that enables maximum likelihood estimation using the EM algorithm. We obtain closed-form expressions for both the E- and M-steps; the former requires the Kalman filter and a fixed-interval smoother, and the latter requires least squares-type regression. We show via simulations that our algorithm converges reliably to the maximum, whereas gradient-based methods often fail because of the highly nonlinear nature of the likelihood function. Moreover, our algorithm converges in a smaller number of function evaluations than commonly used direct-search routines. Overall, our approach achieves its largest performance gains when applied to models of high dimension. We illustrate our technique by estimating a high-dimensional vector moving-average model for an efficiency test of California's wholesale electricity market.
Book Synopsis Likelihood Estimation for Mixture Models Via the EM Algorithm by : Zhenxu Ma
Download or read book Likelihood Estimation for Mixture Models Via the EM Algorithm written by Zhenxu Ma and published by . This book was released on 1996 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Item Response Models Using the EM Algorithm for Finite Mixtures by : David Woodruff
Download or read book Estimation of Item Response Models Using the EM Algorithm for Finite Mixtures written by David Woodruff and published by . This book was released on 1996 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation by : Scott R. Eliason
Download or read book Maximum Likelihood Estimation written by Scott R. Eliason and published by SAGE. This book was released on 1993 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a short introduction to Maximum Likelihood (ML) Estimation. It provides a general modeling framework that utilizes the tools of ML methods to outline a flexible modeling strategy that accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models linking endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, the author discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
Book Synopsis Restricted Maximum Likelihood Estimator for Logistic Regression Models by : Diane E. Duffy
Download or read book Restricted Maximum Likelihood Estimator for Logistic Regression Models written by Diane E. Duffy and published by . This book was released on 1987 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The SAGE Handbook of Multilevel Modeling by : Marc A. Scott
Download or read book The SAGE Handbook of Multilevel Modeling written by Marc A. Scott and published by SAGE. This book was released on 2013-08-31 with total page 954 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Book Synopsis Maximum Likelihood Estimation of Multivariable Dynamic Systems Via the EM Algorithm by : Stuart Harle Gibson
Download or read book Maximum Likelihood Estimation of Multivariable Dynamic Systems Via the EM Algorithm written by Stuart Harle Gibson and published by . This book was released on 2003 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Penalized Likelihood Estimation by : P.P.B. Eggermont
Download or read book Maximum Penalized Likelihood Estimation written by P.P.B. Eggermont and published by Springer Nature. This book was released on 2020-12-15 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
