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Univariate Multivariate General Linear Models

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Univariate And Multivariate General Linear Models

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Author : Kevin Kim
language : en
Publisher: CRC Press
Release Date : 2006-10-11

Univariate And Multivariate General Linear Models written by Kevin Kim and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-11 with Mathematics categories.

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral

Linear Model Theory

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Author : Keith E. Muller
language : en
Publisher: John Wiley & Sons
Release Date : 2006-10-06

Linear Model Theory written by Keith E. Muller 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 2006-10-06 with Mathematics categories.

A precise and accessible presentation of linear model theory, illustrated with data examples Statisticians often use linear models for data analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed linear models separately, whereas Linear Model Theory: Univariate, Multivariate, and Mixed Models presents a unified treatment in order to make clear the distinctions among the three classes of models. Linear Model Theory: Univariate, Multivariate, and Mixed Models begins with six chapters devoted to providing brief and clear mathematical statements of models, procedures, and notation. Data examples motivate and illustrate the models. Chapters 7-10 address distribution theory of multivariate Gaussian variables and quadratic forms. Chapters 11-19 detail methods for estimation, hypothesis testing, and confidence intervals. The final chapters, 20-23, concentrate on choosing a sample size. Substantial sets of excercises of varying difficulty serve instructors for their classes, as well as help students to test their own knowledge. The reader needs a basic knowledge of statistics, probability, and inference, as well as a solid background in matrix theory and applied univariate linear models from a matrix perspective. Topics covered include: A review of matrix algebra for linear models The general linear univariate model The general linear multivariate model Generalizations of the multivariate linear model The linear mixed model Multivariate distribution theory Estimation in linear models Tests in Gaussian linear models Choosing a sample size in Gaussian linear models Filling the need for a text that provides the necessary theoretical foundations for applying a wide range of methods in real situations, Linear Model Theory: Univariate, Multivariate, and Mixed Models centers on linear models of interval scale responses with finite second moments. Models with complex predictors, complex responses, or both, motivate the presentation.

Univariate And Multivariate General Linear Models

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Author : Kevin Kim
language : en
Publisher: CRC Press
Release Date : 2019-12-02

Using a general framework, this book presents analyses of simple and complex models, employing data sets from various disciplines, such as the social and behavioral sciences. This new edition adds two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure. It includes expanded theory on unrestr

Multivariate General Linear Models

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Author : Richard F. Haase
language : en
Publisher: SAGE Publications
Release Date : 2011-11-23

Multivariate General Linear Models written by Richard F. Haase and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-23 with Mathematics categories.

Multivariate General Linear Models is an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the univariate general linear model, this volume defines the key steps in analyzing linear model data, and introduces multivariate linear model analysis as a generalization of the univariate model. The author focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy. The volume concludes with a discussion of canonical correlation analysis that is shown to subsume all the multivariate procedures discussed in previous chapters. The analyses are illustrated throughout the text with three running examples drawing from several disciples, including personnel psychology, anthropology, environmental epidemiology, and neuropsychology.

Multivariate Statistical Modelling Based On Generalized Linear Models

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Author : L. Fahrmeir
language : en
Publisher:
Release Date : 1994

Multivariate Statistical Modelling Based On Generalized Linear Models written by L. Fahrmeir and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Linear models (Statistics) categories.

This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including 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 to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of multivariate and multicategorical models within the generalized linear models framework. Based on well-chosen sets of data, these new developments are introduced to a not necessarily expert audience. Completeness was not an aim. The result is a self-contained, well-written text offering the applied researcher a useful insight into the applicability of the general linear model methodology." P.A.L. Embrechts, ETH-Zentrum, Z rich, Switzerland.

A First Course In Linear Model Theory

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Author : Nalini Ravishanker
language : en
Publisher: CRC Press
Release Date : 2021-10-19

A First Course In Linear Model Theory written by Nalini Ravishanker and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-19 with Mathematics categories.

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality, this second edition features three new chapters and several sections on new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear models, model selection, multiple comparisons, and regularized and robust regression. New to the Second Edition: Coverage of inference for linear models has been expanded into two chapters. Expanded coverage of multiple comparisons, random and mixed effects models, model selection, and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13, and expanded coverage of the Bayesian linear models and longitudinal models. A new section on regularized regression in Chapter 14. Detailed data illustrations using R. The authors' fresh approach, methodical presentation, wealth of examples, use of R, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.

Multivariate Statistical Modelling Based On Generalized Linear Models

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Author : Ludwig Fahrmeir
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Multivariate Statistical Modelling Based On Generalized Linear Models written by Ludwig Fahrmeir 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 2013-11-11 with Mathematics categories.

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.

Handbook Of Univariate And Multivariate Data Analysis With Ibm Spss

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Author : Robert Ho
language : en
Publisher: CRC Press
Release Date : 2013-10-25

Handbook Of Univariate And Multivariate Data Analysis With Ibm Spss written by Robert Ho and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-25 with Mathematics categories.

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics

Beyond Multiple Linear Regression

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Author : Paul Roback
language : en
Publisher: CRC Press
Release Date : 2021-01-14

Beyond Multiple Linear Regression written by Paul Roback and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Mathematics categories.

Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)

Univariate And Multivariate General Linear Models

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Author : Kevin Kim
language : en
Publisher: CRC Press
Release Date : 2006-10-11

Univariate Multivariate General Linear Models

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