A PERFORMANCE MODEL FOR AMERICAN MANUFACTURING AND SERVICE JOINT VENTURES IN SAUDI ARABIA

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" A PERFORMANCE MODEL FOR AMERICAN MANUFACTURING AND SERVICE JOINT VENTURES IN SAUDI ARABIA "
A. Y. Al-Aali

Document Type	:	BL
Record Number	:	655931
Doc. No	:	dltt
Main Entry	:	Verbeke, Geert
Title & Author	:	Linear mixed models for longitudinal data /\ Geert Verbeke, Geert Molenberghs
Series Statement	:	Springer series in statistics
Page. NO	:	xxii, 568 pages :: illustrations ;; 25 cm
ISBN	:	0387982221
	:	: 9780387982229
	:	: 0387950273
	:	: 9780387950273
Bibliographies/Indexes	:	Includes bibliographical references (pages 523-553) and index
Contents	:	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 -- App. B. Technical Details for Sensitivity Analysis
Abstract	:	Provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mid models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. How3ever, some other commercially available packages are discu