Analysis of Longitudinal Data

Nonfiction, Science & Nature, Mathematics, Statistics, Health & Well Being, Medical
Cover of the book Analysis of Longitudinal Data by Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger, OUP Oxford
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Author: Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger ISBN: 9780191664335
Publisher: OUP Oxford Publication: March 14, 2013
Imprint: OUP Oxford Language: English
Author: Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger
ISBN: 9780191664335
Publisher: OUP Oxford
Publication: March 14, 2013
Imprint: OUP Oxford
Language: English

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

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The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

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