Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.
Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites as well as through the \texttt{R} package \texttt{GLMpack}. All links are available at www.sagepub.com/gill2e.
SInce the Spring of 2016 all of the code and data for the book has been located in the
R package BaM, including both R and JAGS. Answers to odd numbered exercises are
here. The
Corrected index for the very first print run (few people will need this). The state.df dataset got left off the BaM package so it's
here. The errata for the third edition are
here."A compact guide to the voluminous literature on optimisation,
numerical analysis, and computational statistics. This is no small achievement." -
Statistical Software Newsletter in Computational Statistics and Data Analysis.See also the review in JASA, June 2005.
Software Resources,
Table of Contents and Two Sample Chapters,
Technometrics Review,
ACM Review,
SciTech Book News Review,
Choice Review,
Computational Statistics and Data Analysis Review,
Journal of Statistical Software Review,
Statistical Methods in Medical Research Review,
Journal of Statistical Computation and Simulation Review,
Corrigenda.
"...easily the most comprehensive, scholarly, and thoughtful book the subject... it will do much to promote the use of Bayesian methods." -David Blackwell, University of California, Berkeley.
"A surprisingly thorough review written by a user of Bayesian statistics, with applications drawn from the social sciences." -Persi Diaconis, Stanford University.
"This book is a brilliant and importantly very accessible introduction to the concept and application of Bayesian approaches to data analysis. The clear strength of the book is in making the concept practical and accessible, without necessarily dumbing it down. It therefore retains the richness and complexity of this approach. The coverage is also remarkable, especially discussing this approach in the context of hierarchical linear models." -S V Subramanian, Harvard University.
