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020			_a9780387950273
082			_a519.5 _bVER
100			_aVerbeke, Geert _932235
245			_aLinear mixed models for longitudinal data
260			_aNew York : _bSpringer, _c2000
300			_axxii, 568 p. ; _c24 cm.
440			_aSpringer Series in Statistics _932820
500			_aContent : 1. Introduction -- 2. Examples -- 3. A Model for Longitudinal Data -- 4. Exploratory Data Analysis -- 5. Estimation of the Marginal Model -- 6. Inference for the Marginal Model -- 7. Inference for the Random Effects -- 8. Fitting Linear Mixed Models with SAS -- 9. General Guidelines for Model Building -- 10. Exploring Serial Correlation -- 11. Local Influence for the Linear Mixed Model -- 12. The Heterogeneity Model -- 13. Conditional Linear Mixed Models -- 14. Exploring Incomplete Data -- 15. Joint Modeling of Measurements and Missingness -- 16. Simple Missing Data Methods -- 17. Selection Models -- 18. Pattern-Mixture Models -- 19. Sensitivity Analysis for Selection Models -- 20. Sensitivity Analysis for Pattern-Mixture Models -- 21. How Ignorable Is Missing At Random? -- 22. The Expectation-Maximization Algorithm -- 23. Design Considerations -- 24. Case Studies --
520			_aThe SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.
650			_aLongitudinal method _932821
650			_aMathematics _932822
650			_aLinear models (Statistics) _932823
700			_aMolenberghs, Geert _932824
942			_cIM
999			_c186562 _d186562