An Introduction to Generalized Linear Models, 4th

Dobson, Annette J., and Barnett, Adrian G.

ISBN 10: 113874168X / ISBN 13: 9781138741683

Verlag: CRC Press, London, 2018

Beschreibung

***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service. 376 p. Chapman & Hall/CRC Texts in Statistical Science. Audience: College/higher education.

Bestandsnummer des Verkäufers K582DA0001483

Bibliografische Details

• Titel: An Introduction to Generalized Linear Models, 4th
• Verlag: CRC Press, London
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 113874168X
• ISBN-13: 9781138741683
• Einband: Trade paperback
• Auflage: 4th ed. INTERNATIONAL EDITION
• Zustand: New in new dust jacket.

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.

Adrian G. Barnett is a professor at the Queensland University of Technology.