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Robust Linear Mixed Models With Skew Normal Independent Distributions From A Bayesian Perspective
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Book Synopsis Robust Linear Mixed Models with Skew-normal Independent Distributions from a Bayesian Perspective by : V. H. Lachos
Download or read book Robust Linear Mixed Models with Skew-normal Independent Distributions from a Bayesian Perspective written by V. H. Lachos and published by . This book was released on 2008 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Linear Mixed Model Using the Skew-t Distribution by : C. L. Bayes
Download or read book Robust Linear Mixed Model Using the Skew-t Distribution written by C. L. Bayes and published by . This book was released on 2007 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Skew-Normal Model Theories and Their Applications by : Rendao Ye
Download or read book Skew-Normal Model Theories and Their Applications written by Rendao Ye and published by CRC Press. This book was released on 2024-11-08 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on several skew-normal mixed effects models, and systematically explores statistical inference theories, methods, and applications of parameters of interest. This book is of academic value as it helps to establish a series of statistical inference theories and methods for skew-normal mixed effects models. On the applications side, it provides efficient methods and tools for practical data analysis in various fields including economics, finance, biology and medical science.
Book Synopsis Robust Mixed Model Analysis by : Jiang Jiming
Download or read book Robust Mixed Model Analysis written by Jiang Jiming and published by World Scientific. This book was released on 2019-04-10 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models.This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications.
Book Synopsis Monte-Carlo Simulation-Based Statistical Modeling by : Ding-Geng (Din) Chen
Download or read book Monte-Carlo Simulation-Based Statistical Modeling written by Ding-Geng (Din) Chen and published by Springer. This book was released on 2017-02-01 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
Book Synopsis Innovations in Multivariate Statistical Modeling by : Andriëtte Bekker
Download or read book Innovations in Multivariate Statistical Modeling written by Andriëtte Bekker and published by Springer Nature. This book was released on 2022-12-15 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.
Book Synopsis Generalized Linear Models by : Dipak K. Dey
Download or read book Generalized Linear Models written by Dipak K. Dey and published by CRC Press. This book was released on 2000-05-25 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers
Book Synopsis Generalized Linear Mixed Models by : Charles E. McCulloch
Download or read book Generalized Linear Mixed Models written by Charles E. McCulloch and published by IMS. This book was released on 2003 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models
Book Synopsis Finite Mixture of Skewed Distributions by : Víctor Hugo Lachos Dávila
Download or read book Finite Mixture of Skewed Distributions written by Víctor Hugo Lachos Dávila and published by Springer. This book was released on 2018-11-12 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent results in finite mixtures of skewed distributions to prepare readers to undertake mixture models using scale mixtures of skew normal distributions (SMSN). For this purpose, the authors consider maximum likelihood estimation for univariate and multivariate finite mixtures where components are members of the flexible class of SMSN distributions. This subclass includes the entire family of normal independent distributions, also known as scale mixtures of normal distributions (SMN), as well as the skew-normal and skewed versions of some other classical symmetric distributions: the skew-t (ST), the skew-slash (SSL) and the skew-contaminated normal (SCN), for example. These distributions have heavier tails than the typical normal one, and thus they seem to be a reasonable choice for robust inference. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn, highlighting the applicability of the techniques presented in the book. This work is a useful reference guide for researchers analyzing heterogeneous data, as well as a textbook for a graduate-level course in mixture models. The tools presented in the book make complex techniques accessible to applied researchers without the advanced mathematical background and will have broad applications in fields like medicine, biology, engineering, economic, geology and chemistry.
Book Synopsis Bayesian Analysis of Linear Models by : Broemeling
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by CRC Press. This book was released on 1984-12-06 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Book Synopsis Approximate Inferences for Nonlinear Mixed-effects Models with Skew-normal Independent Distributions by : Victor H. Lachos
Download or read book Approximate Inferences for Nonlinear Mixed-effects Models with Skew-normal Independent Distributions written by Victor H. Lachos and published by . This book was released on 2009 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian inference for skew-normal linear mixed models by : R. B. Arellano-Valle
Download or read book Bayesian inference for skew-normal linear mixed models written by R. B. Arellano-Valle and published by . This book was released on 2004 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Mixed Models in Practice by : Geert Verbeke
Download or read book Linear Mixed Models in Practice written by Geert Verbeke and published by Springer. This book was released on 1997-08-07 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive treatment of linear mixed models, focusing on examples from designed experiments and longitudinal studies. Aimed at applied statisticians and biomedical researchers in industry, public health organisations, contract research organisations, and academia, this book is explanatory rather than mathematical rigorous. Although most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated, considerable effort was put into presenting the data analyses in a software-independent fashion.
Book Synopsis Joint Models for Longitudinal and Time-to-Event Data by : Dimitris Rizopoulos
Download or read book Joint Models for Longitudinal and Time-to-Event Data written by Dimitris Rizopoulos and published by CRC Press. This book was released on 2012-06-22 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/
Book Synopsis Bayesian Analysis of Linear Models by : Lyle D. Broemeling
Download or read book Bayesian Analysis of Linear Models written by Lyle D. Broemeling and published by . This book was released on 1985 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multivariate Statistical Modelling Based on Generalized Linear Models by : Ludwig Fahrmeir
Download or read book Multivariate Statistical Modelling Based on Generalized Linear Models written by Ludwig Fahrmeir and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concerned with the use of generalised linear models for univariate and multivariate regression analysis, this is a detailed introductory survey of the subject, based on the analysis of real data drawn from a variety of subjects such as the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account.
Book Synopsis Generalized Linear Mixed Models by : Walter W. Stroup
Download or read book Generalized Linear Mixed Models written by Walter W. Stroup and published by CRC Press. This book was released on 2024-05-21 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory. Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs. Key Features: • Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family – classical and advanced models. • Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices. • Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design. • Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate. • In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs. Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions. Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute." Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor’s degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
