An introduction to generalized linear models / Annette J. Dobson, Adrian G Barnett.

By: Dobson, Annette J, 1945-
Contributor(s): Barnett, Adrian G
Material type: TextTextLanguage: English Series: Texts in statistical science: Publisher: Boca Raton : CRC Press, c2008Edition: 3rd edDescription: 307 p. : ill. ; 24 cmISBN: 9781584889502 (alk. paper); 1584889500 (alk. paper)Subject(s): Linear models (Statistics)DDC classification: 519.5 LOC classification: QA276 | .D589 2008Online resources: Table of contents only | WorldCat Details | E-book Fulltext
Contents:
Table of contents 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.
Summary: Summary: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.
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Introduction to Generalized Linear Models

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Includes bibliographical references (p. [295]-301) and index.

Table of contents 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.

Summary:
Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.

Applied Statistics

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