Generalized Linear Models and Extensions, Second Edition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Generalized Linear Models and Extensions, Second Edition write by James W. Hardin. This book was released on 2007. Generalized Linear Models and Extensions, Second Edition available in PDF, EPUB and Kindle. Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.
Generalized Linear Models and Extensions
Generalized Linear Models and Extensions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Generalized Linear Models and Extensions write by James William Hardin. This book was released on 2012. Generalized Linear Models and Extensions available in PDF, EPUB and Kindle.
Linear Models with R
Linear Models with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Linear Models with R write by Julian J. Faraway. This book was released on 2016-04-19. Linear Models with R available in PDF, EPUB and Kindle. A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
Generalized Linear Models for Insurance Data
Generalized Linear Models for Insurance Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Generalized Linear Models for Insurance Data write by Piet de Jong. This book was released on 2008-02-28. Generalized Linear Models for Insurance Data available in PDF, EPUB and Kindle. This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.
Foundations of Linear and Generalized Linear Models
Foundations of Linear and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Linear and Generalized Linear Models write by Alan Agresti. This book was released on 2015-02-23. Foundations of Linear and Generalized Linear Models available in PDF, EPUB and Kindle. A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
