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Foundations of Linear and Generalized Linear Models (Wiley Series in Probability and Statistics), by Alan Agresti
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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
�
- Sales Rank: #244590 in Books
- Published on: 2015-02-23
- Original language: English
- Number of items: 1
- Dimensions: 9.55" h x 1.15" w x 6.45" l, .0 pounds
- Binding: Hardcover
- 472 pages
Review
"The book arose from a one-semester graduate level course taught by Alan Agresti at Harvard University. It has a clear didactic focus, which benefits greatly from Agresti’s well-known clear writing style. Each of the 11 chapters is followed by around 40 exercises, which are diverse and interesting."
"...I am very happy with the foundational perspective of this book. I think that students who master this material will have a very thorough understanding of the most important aspects of GLMs, which is more valuable than a kaleidoscopic knowledge. This is certainly one of the books I will consider when next I need to teach a course in generalized linear models."
"...this is a great introduction to GLMs written in a clear and didactic style, and with a thoughtful choice and presentation of the material. Highly recommended."
--Biometrics Journal, 2016
�
From the Back 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 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 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.
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.Most helpful customer reviews
7 of 7 people found the following review helpful.
Very well written with plenty of examples and R code ...
By Napo
Very well written with plenty of examples and R code. It contains some theoretical background (providing proofs and derivations) as well as applications. However, since it covers a wide spectrum (it provides both frequentist and Bayesian perspectives) it doesn't duel deeply in some subjects. At the end of each chapter there are bibliographical references for those interested in further reading. Very valuable resource.
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