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Maximum Likelihood Estimation And Inference


Maximum Likelihood Estimation And Inference
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Maximum Likelihood Estimation And Inference


Maximum Likelihood Estimation And Inference
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Author : Russell B. Millar
language : en
Publisher: John Wiley & Sons
Release Date : 2011-07-26

Maximum Likelihood Estimation And Inference written by Russell B. Millar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-26 with Mathematics categories.


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.



Estimation Inference And Specification Analysis


Estimation Inference And Specification Analysis
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Author : Halbert White
language : en
Publisher: Cambridge University Press
Release Date : 1996-06-28

Estimation Inference And Specification Analysis written by Halbert White and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-06-28 with Business & Economics categories.


This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated.



Maximum Likelihood Estimation And Inference For High Dimensional Nonlinear Factor Models


Maximum Likelihood Estimation And Inference For High Dimensional Nonlinear Factor Models
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Author : Fa Wang
language : en
Publisher:
Release Date : 2017

Maximum Likelihood Estimation And Inference For High Dimensional Nonlinear Factor Models written by Fa Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.




Maximum Likelihood Estimation And Inference For High Dimensional Generalized Factor Models With Application To Factor Augmented Regressions


Maximum Likelihood Estimation And Inference For High Dimensional Generalized Factor Models With Application To Factor Augmented Regressions
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Author : Fa Wang
language : en
Publisher:
Release Date : 2021

Maximum Likelihood Estimation And Inference For High Dimensional Generalized Factor Models With Application To Factor Augmented Regressions written by Fa Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.


This paper reestablishes the main results in Bai (2003) and Bai and Ng(2006) for generalized factor models, with slightly stronger conditions on therelative magnitude of N(number of subjects) and T(number of time periods).Convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mildconditions that allow for linear, Logit, Probit, Tobit, Poisson and some othersingle-index nonlinear models. The probability density/mass function is allowed to vary across subjects and time, thus mixed models are also allowed for.For factor-augmented regressions, this paper establishes the limit distributionsof the parameter estimates, the conditional mean, and the forecast when factorsestimated from nonlinear/mixed data are used as proxies for the true factors.



Verzeichnis Der Ver Ffentlichungen Von Herbert Franke


Verzeichnis Der Ver Ffentlichungen Von Herbert Franke
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Author :
language : en
Publisher:
Release Date : 1979*

Verzeichnis Der Ver Ffentlichungen Von Herbert Franke written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979* with categories.




Statistical Inference Based On The Likelihood


Statistical Inference Based On The Likelihood
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Author : Adelchi Azzalini
language : en
Publisher: Routledge
Release Date : 2017-11-13

Statistical Inference Based On The Likelihood written by Adelchi Azzalini and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Mathematics categories.


The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood. Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.



Applied Statistical Inference


Applied Statistical Inference
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Author : Leonhard Held
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-12

Applied Statistical Inference written by Leonhard Held and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-12 with Mathematics categories.


This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.



Maximum Likelihood Estimation For Sample Surveys


Maximum Likelihood Estimation For Sample Surveys
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Author : Raymond L. Chambers
language : en
Publisher: CRC Press
Release Date : 2012-05-02

Maximum Likelihood Estimation For Sample Surveys written by Raymond L. Chambers and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-02 with Mathematics categories.


Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to



Statistical Inference


Statistical Inference
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Author : Ayanendranath Basu
language : en
Publisher: CRC Press
Release Date : 2011-06-22

Statistical Inference written by Ayanendranath Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-22 with Computers categories.


In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Stati



Methods For Estimation And Inference In Modern Econometrics


Methods For Estimation And Inference In Modern Econometrics
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Author : Stanislav Anatolyev
language : en
Publisher: CRC Press
Release Date : 2011-06-07

Methods For Estimation And Inference In Modern Econometrics written by Stanislav Anatolyev and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-07 with Business & Economics categories.


This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.