Modelling frequency and count data / J.K. Lindsey.
By: Lindsey, James K
Material type: 
TextLanguage: English Series: Oxford statistical science series ; 15Publisher: Oxford : New York : Clarendon Press ; Oxford University Press, 1995Description: ix, 291 p. : ill. ; 24 cmISBN: 0198523319 :Subject(s): Multivariate analysisDDC classification: 519.536 LOC classification: QA278 | .L5538 1995Online resources: Publisher description | Table of contents only | Worldcat details | E-book Fulltext Summary: Categorical data analysis is a special area of generalized linear models which has become the most important area of statistical applications in many disciplines, from medicine to social science. Written for advanced undergraduates, but also useful for statisticians and research workers, this text presents the standard models as well as many newly developed ones in a language which can be applied in many modern statistical packages such as GLIM, GENMSTAT, S-Plus, and SAS and LISP-STAT. Structured around the distinction between independent events occurring to different individuals and repeated events occurring to the same individuals, the book demonstrates that much of modern statistics can be seen as special cases of categorical data models. Other topics covered include Markov chains, overdispersion, and random effects.
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Includes bibliographical references (p. 275-282) and index.
Categorical data analysis is a special area of generalized linear models which has become the most important area of statistical applications in many disciplines, from medicine to social science. Written for advanced undergraduates, but also useful for statisticians and research workers, this text presents the standard models as well as many newly developed ones in a language which can be applied in many modern statistical packages such as GLIM, GENMSTAT, S-Plus, and SAS and LISP-STAT. Structured around the distinction between independent events occurring to different individuals and repeated events occurring to the same individuals, the book demonstrates that much of modern statistics can be seen as special cases of categorical data models. Other topics covered include Markov chains, overdispersion, and random effects.
Applied Statistics
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