Analysis of Longitudinal Data

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Opis: Analysis of Longitudinal Data - Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty

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.1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index


Szczegóły: Analysis of Longitudinal Data - Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty

Tytuł: Analysis of Longitudinal Data
Autor: Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty
Producent: Oxford University Press
ISBN: 9780199676750
Rok produkcji: 2013
Ilość stron: 400
Oprawa: Miękka
Waga: 0.59 kg


Recenzje: Analysis of Longitudinal Data - Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty

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Analysis of Longitudinal Data

, ,

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.1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index

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Cena 158,60 PLN
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Oszczędzasz 5%
Wysyłka: Niedostępna
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Szczegóły: Analysis of Longitudinal Data - Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty

Tytuł: Analysis of Longitudinal Data
Autor: Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty
Producent: Oxford University Press
ISBN: 9780199676750
Rok produkcji: 2013
Ilość stron: 400
Oprawa: Miękka
Waga: 0.59 kg


Recenzje: Analysis of Longitudinal Data - Scott L. Zeger, Kung-Yee Liang, Patrick J. Heagerty

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