Foundations of Linear and Generalized Linear Models

Foundations of Linear and Generalized Linear Models – Agresti – 2015

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Series: Wiley Series in Probability and Statistics
Publisher: Wiley; 1 edition (February 23, 2015)
Language: English
ISBN-10: 1118730038
ISBN-13: 978-1118730034

Review

“An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, the book 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.”  (Zentralblatt MATH, 1 June 2015)

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.

About the Author

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 35 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed article and five texts including “Statistical Methods for the Social Sciences” (with Barbara Finlay, Prentice Hall, 4th edition 2009) and “Categorical Data Analysis” (Wiley, 2nd edition 2002). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003 Alan was named “Statistician of the Year” by the Chicago chapter of the American Statistical Association and in 2004 he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 30 countries worldwide. Alan has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons. Christine Franklin is a Senior Lecturer and Honors Professor in the Department of Statistics at the University of Georgia. She has been a member of college faculty in statistics for almost 30 years. Chris has been actively involved at the national level with promoting statistical education at the K-12 level and college undergraduate level since the 1980’s. She is currently the Chief Reader for AP Statistics and has developed threemasters level courses at UGA in data analysis for elementary, middle school, and secondary teachers. Chris was the lead writer for the ASA endorsed Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K-12 Curriculum Framework. Chris has been honored by her selection as a Fellow of the American Statistical Association, the 2006 Mu Sigma Rho National Statistical Education Award recipient for her teaching and lifetime devotion to statistics education, and numerous teaching and advising awards at UGA. Chris has written more than 30 journal articles and resource materials for textbooks.
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