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Review
—Pharmaceutical Statistics, 2011
The chapters are short and concise, and the writing is clear … explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians.
—Biometrics
This book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. … Furthermore, the text covers important topics that are frequently overlooked in introductory courses, such as models for ordinal outcomes. … This book is an excellent resource, either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets.
—Journal of Biopharmaceutical Statistics, Issue 2
Praise for the Second Edition
The second edition … is successful in filling a void in the otherwise sparse literature on the subject of generalized linear models at the introductory level … a wide range of research applications are covered and ample workings are also provided to aid the reader in statistical calculations … I would highly recommend this text … .
—Kerrie Nelson, Statistics in Medicine, Vol. 23
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Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
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This book is designed to introduce the reader to generalized linear models; these provide a unifying framework for many commonly used statistical techniques. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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The only thing worth flagging from my own experience (as a post-grad physician) is that mathematical proofs are prominent. These sail largely over my head, but the authors do a good job of keeping the main points comprehensible to those of who feel a faint sense of regret at knowledge lost when the maths really gets going.
The text is mathematical in nature, but not extremely so.
The requirements are as far as I can see introduction courses in statistics, calculus and matrix algebra.
Almost all methods are illustrated with numerical examples. Data sets an solution outlines are available from the internet, making the book suitable for self study.
The only small point of critique is that I feel that the explanation of the numerical techniques used in the estimation of GLM's might be expanded (or additional material could be provided in appendix or on te internet).
The wait seems to have been very worthwhile. The second edition is a real bargin.... She has updated it with the many advances that have occurred over the past 12 years since the first edition was printed. This edition now includes some discussion of generalized additive models, broader coverage of applications as survival analysis, GEE, multi-level models and nominal and ordinal logistic regression have been added. It now offers the reader more applications in a wider variety of disciplines and includes modern approaches to diagnostic checking of the models.
As with the first edition, exploratory techniques are emphasized particularly graphical methods. The goal is to unify the apparently disparate statistical techniques that students are exposed to, into one general modeling framework.
It includes a nice up-to-date bibliography and recent advanced results on longitudinal models. The level is intermediate statistics with introductory statistics and linear models taken to be prerequisites. Students are also required to have some familiarity with calculus and linear algebra.
Thus, while this book is not ideal for someone who wants to jump right into the thick of building logistic, loglinear, or other models for nominal data, it is quite suitable for those wishing a thorough introduction to the practice of generalized linear modeling. For greater detail, a thicker book like McCullagh & Nelder's _Generalized Linear Models_ would be suitable.
Note: While the term "Generalized Linear Models" includes linear regression models (i.e., models for continuous dependent variables), reading this book is not the easiest way to be introduced to regression. A better starting point would be Draper & Smith's _Applied Regression Analysis_ or Weisberg's _Applied Linear Regression_.
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