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- Author: J K Lindsey
- Publisher: Clarendon Press
- ISBN-10: 0198523319
- ISBN-13: 9780198523314
- Format: 16.1 x 23.6 x 2.5 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
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 sciences. This text presents the standard models and many newly developed ones in a language that can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstrates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered.
EXTRA 10 % discount with code: EXTRA
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- Author: J K Lindsey
- Publisher: Clarendon Press
- ISBN-10: 0198523319
- ISBN-13: 9780198523314
- Format: 16.1 x 23.6 x 2.5 cm, hardcover
- Language: English English
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 sciences. This text presents the standard models and many newly developed ones in a language that can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstrates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered.
Reviews