-PDF Download- Multivariate General Linear Models EBOOK

Multivariate Statistical Modelling Based on Generalized Linear Models

Author: Ludwig Fahrmeir

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 440

ISBN-13: 1489900101

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.

Multivariate General Linear Models

Author: Richard F. Haase

Publisher: SAGE Publications

Published: 2011-11-23

Total Pages: 225

ISBN-13: 1483303721

DOWNLOAD EBOOK →

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.

Univariate and Multivariate General Linear Models

Author: Kevin Kim

Publisher: CRC Press

Published: 2006-10-11

Total Pages: 576

ISBN-13: 1000738256

DOWNLOAD EBOOK →

Univariate and Multivariate General Linear Models

Author: Kevin Kim

Publisher: CRC Press

Published: 2006-10-11

Total Pages: 576

ISBN-13: 9781584886341

DOWNLOAD EBOOK →

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 sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.

Beyond Multiple Linear Regression

Author: Paul Roback

Publisher: CRC Press

Published: 2021-01-14

Total Pages: 436

ISBN-13: 1439885400

DOWNLOAD EBOOK →

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)

Multivariate Generalized Linear Mixed Models Using R

Author: Damon Mark Berridge

Publisher: CRC Press

Published: 2011-04-25

Total Pages: 284

ISBN-13: 1439813272

DOWNLOAD EBOOK →

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.A Un

Univariate and Multivariate General Linear Models

Author: Kevin Kim

Publisher: CRC Press

Published: 2019-12-02

Total Pages: 549

ISBN-13: 9780367453442

DOWNLOAD EBOOK →

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

Applying Generalized Linear Models

Author: James K. Lindsey

Publisher: Springer Science & Business Media

Published: 2008-01-15

Total Pages: 265

ISBN-13: 038722730X

DOWNLOAD EBOOK →

This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized Linear Models

Author: P. McCullagh

Publisher: Routledge

Published: 2019-01-22

Total Pages: 361

ISBN-13: 1351445847

DOWNLOAD EBOOK →

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot