Generalized Linear Models (Monographs on Statistics and Applied Probability)

McCullagh, P.:

ISBN 10: 0412238500 / ISBN 13: 9780412238505

Published by Chapman and Hall, New York, 1983

About this Item

xiii, 261 pp. with a lot of figures, 0412238500 Sprache: Englisch Gewicht in Gramm: 380 8°, Original-Pappband (Hardcover), Bibliotheks-Exemplar (ordnungsgemäß entwidmet) mit leichten Rückständen einer Papier-Signatur, Stempel auf Titel, insgesamt gutes und innen sauberes Exemplar, (library copy in good condition),

Seller Inventory # 126021

Bibliographic Details

• Title: Generalized Linear Models (Monographs on Statistics and Applied Probability)
• Publisher: Chapman and Hall, New York
• Publication Date: 1983
• Language: English
• ISBN 10: 0412238500
• ISBN 13: 9780412238505
• Binding: Hardcover
• Weight: 380 grams
• Seller catalogs:
  • Mathematik

About this title

Synopsis

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications.

The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables.

The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions.

Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.