Models for Discrete Data
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Discrete or count data arise in experiments where the outcome variables are the numbers of individual 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 Several new datasets, mostly from the health and medical sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.
Features
- This revised edition contains 54% more exercises (including 84% more applied exercises) to encourage exploration of the subject.
- Many numerical examples to illustrate the statistical theory
- Programs in SAS to show the reader how to perform their own analysis and teach the software
- Sections on power and sample size estimation address one of the most difficult and frequently asked questions
Reviews
"Models for Discrete Data is a refreshing applied statistics book, refreshing for its clear presentation, style and contents. ... This is an applied statistics book that every serious statistician, especially a student, should have on his desk."--Mathematical Association of America
"One of this book's greatest strengths is its inclusion of a large number of useful exercises, each categorized as either theoretical or applied. These problems greatly enhance the book's value as a course text, particularly for a master's level graduate course oriented primarily toward applications rather than theory."--Journal of the American Statistical Association
About the Author(s)
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.
