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Foundations of Linear and Generalized Linear Models (Wiley Series in Probability and Statistics) Hardback - 2015
by Agresti, Alan
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- Good
- Hardback
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Details
- Title Foundations of Linear and Generalized Linear Models (Wiley Series in Probability and Statistics)
- Author Agresti, Alan
- Binding Hardback
- Edition US Edition
- Condition Used - Good
- Pages 480
- Volumes 1
- Language ENG
- Publisher Wiley
- Publication date 2015-02-24
- Features Bibliography, Index
- Bookseller's Inventory # 1118730038.G
- 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)
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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 1
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From the publisher
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.
