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Analysis of longitudinal data / Peter J. Diggle ... [et al.].

Contributor(s): Diggle, Peter.
Material type: TextTextSeries: Oxford statistical science series ; no. 25.Publisher: Oxford : Oxford University Press, 2013Edition: 2nd ed.Description: p. cm.ISBN: 9780199676750 (pbk.); 0199676755 (pbk.); 9780198524847; 9780199676750.Subject(s): Longitudinal method | Time-series analysis | Multivariate analysisDDC classification: 610.72 ANA Online resources: OCLC | E-book Fulltext
Contents:
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
Summary: This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
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Previous ed.: 2002.

Includes bibliographical references and index.

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

This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.

Computer Science & Engineering

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