An Introduction to Generalized Linear Models, 3rd

Dobson, Annette J, and Barnett, Adrian G

ISBN 10: 1584889500 / ISBN 13: 9781584889502

Verlag: Chapman & Hall/CRC, Boca Raton, FL, 2008

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. Trade paperback (US). Glued binding. 307 p. Contains: Illustrations, black & white, Tables, black & white, Figures. Texts in Statistical Science (Chapman & Hall/CRC). Audience: General/trade.

Bestandsnummer des Verkäufers K5820000995

Bibliografische Details

• Titel: An Introduction to Generalized Linear Models, 3rd
• Verlag: Chapman & Hall/CRC, Boca Raton, FL
• Erscheinungsjahr: 2008
• Sprache: Englisch
• ISBN-10: 1584889500
• ISBN-13: 9781584889502
• Einband: Trade paperback
• Auflage: 3rd ed. INTERNATIONAL EDITION
• Zustand: New in new dust jacket.

Über diesen Titel

Inhaltsangabe

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis.

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.

Críticas

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

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