Articles liés à Models for Discreet Data

Models for Discreet Data - Couverture rigide

Zelterman, David

 
9780198524366: Models for Discreet Data

Synopsis

Discrete data come about in experiments in which individuals are classified into one of several discrete categories. For example, laboratory mice can simultaneously be categorized as to whether or not they were exposed to a carcinogen and whether or not they subsequently developed cancer. This volume describes the statistical models for the analysis and summary of such data, along with relevant computer programmes written in SAS.

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Présentation de l'éditeur

Discrete or count data arise in experiments where the outcome variables are the numbers of individuals classified into unique, non-overlapping categories. This revised edition describes the statistical models used in the analysis and summary of such data, and provides a sound introduction to the subject for graduate students and practitioners needing a review of the methodology. With many numerical examples throughout, it includes topics not covered in depth elsewhere, such as the negative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free models. A detailed treatment of sample size estimation and power are given in terms of both exact inference and asymptotic, non-central chi-squared methods. A new section covering Poisson regression has also been included. An important feature of this book, missing elsewhere, is the integration of the software into the text. Many more exercises are provided (including 84% more applied exercises) than in the previous edition, helping consolidate the reader's understanding of all subjects covered, and making the book highly suitable for use in a classroom setting. Several new datasets, mostly from the health and medical sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.

Biographie de l'auteur

Daniel Zelterman is Professor of Biostatistics in the Yale School of Public Health and Director of the Biostatistics Core of the Yale Comprehensive Cancer Center. He previously held academic positions at the University of Minnesota and at the State University of New York at Albany. He is an elected Fellow of the American Statistical Association. He is an Associate Editor of Biometrics and several other statistical journals.

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