Details

Title Foundations of Linear and Generalized Linear Models
Author Alan Agresti
Binding Hardback
Edition US Edition
Condition New
Pages 480
Volumes 1
Language ENG
Publisher Wiley
Publication date 2015-02-24
Features Bibliography, Index
Bookseller's Inventory # THWL-28933
ISBN 9781118730034 / 1118730038
Weight 1.72 lbs (0.78 kg)
Dimensions 9.3 x 6.2 x 1.1 in (23.62 x 15.75 x 2.79 cm)
Themes
- Aspects (Academic): Science/Technology Aspects
Category Mathematics
Library of Congress subjects Linear models (Statistics), Mathematical analysis - Foundations
Library of Congress Catalogue Number 2014036543
Dewey Decimal Code 003.74
Quantity available 3

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From the publisher

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.

From the rear cover

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 linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building.

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 of Linear 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.

About the author

ALAN AGRESTI, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on generalized linear models and categorical data methods in more than 30 countries. The author of over 200 journal articles, Dr. Agresti is also the author of Categorical Data Analysis, Third Edition, Analysis of Ordinal Categorical Data, Second Edition, and An Introduction to Categorical Data Analysis, Second Edition, all published by Wiley.

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