An Introduction to Generalized Linear Models

 
 
CRC Press
  • erschienen am 11. April 2018
 
  • Buch
  • |
  • Softcover
  • |
  • 376 Seiten
978-1-138-74151-5 (ISBN)
 
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.

Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them

Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis

Connects Bayesian analysis and MCMC methods to fit GLMs

Contains numerous examples from business, medicine, engineering, and the social sciences

Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods

Offers the data sets and solutions to the exercises online

Describes the components of good statistical practice to improve scientific validity and reproducibility of results.

Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
4 New edition
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • Neue Ausgabe
  • 73 s/w Zeichnungen, 114 s/w Tabellen, 73 s/w Abbildungen
  • |
  • 73 Line drawings, black and white; 114 Tables, black and white; 73 Illustrations, black and white
  • Höhe: 163 mm
  • |
  • Breite: 429 mm
  • |
  • Dicke: 27 mm
  • 596 gr
978-1-138-74151-5 (9781138741515)
1-138-74151-5 (1138741515)
weitere Ausgaben werden ermittelt
Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.
Adrian G. Barnett is a professor at the Queensland University of Technology.
Introduction
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
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) 53

The 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, 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
 

<strong>Praise for the Third Edition:</strong>


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, <em>Statistical Papers</em> (2012) 53

The 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 ... .
-<em>Pharmaceutical Statistics</em>, 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.
-<em>Biometrics</em>


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.
-<em>Journal of Biopharmaceutical Statistics</em>, Issue 2


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