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An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
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.
Show moreAn Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
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.
Show moreIntroduction. Model Fitting. Exponential Family and Generalized. Linear Models.Estimation. Inference. Normal Linear Models. Binary Variables and Logistic Regression. Nominal and Ordinal Logistic Regression. Poisson Regression and Log-Linear Models.Survival Analysis. Clustered and Longitudinal Data. Bayesian Analysis. Markov Chain Monte Carlo Methods. Example Bayesian Analyses. Postface. Appendix.
Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.
Adrian G. Barnett is a professor at the Queensland University of Technology.
Praise for the Third Edition:Overall, this new edition remains a
highly useful and compact introduction to a large number of
seemingly disparate regression models. Depending on the background
of the audience, it will be suitable for upper-level undergraduate
or beginning post-graduate courses.
—Christian Kleiber, Statistical Papers (2012) 53The comments of
Lang in his review of the second edition, that ‘This relatively
short book gives a nice introductory overview of the theory
underlying generalized linear modelling. …’ can equally be applied
to the new edition. … three new chapters on Bayesian analysis are
also added. … suitable for experienced professionals needing to
refresh their knowledge … .
—Pharmaceutical Statistics, 2011The 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.
—BiometricsThis 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 2This book aims to
provide an overview of the key issues in generalized linear models
(GLMs), including assumptions, estimation methods, different link
functions, and a Bayesian approach. Applications of the book
concern different types of data, such as continuous, categorical,
count, correlated, and time-to-event data. The book contains
theoretical and applicable examples of different type of GLMs. The
first five chapters introduce the basics of linear models and the
relations between different distributions. The following chapters
explain GLMs in respect to different types of link function. One of
the most important features of the book is the statistical software
codes in each chapter, which make it more practical, as well as the
last chapter that focuses on examples of Bayesian analysis.
- Morteza Hajihosseini in ISCB, June 2019
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