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Linear mixed models for longitudinal data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Linear mixed models for longitudinal data/ Geert Verbeke, Geert Molenberghs.
作者:	Verbeke, Geert.
其他作者:	Molenberghs, Geert.
出版者:	New York :Springer, : c2000.,
面頁冊數:	xxii, 568 p. :ill. ;25 cm.
叢書名:	Springer series in statistics
內容註:	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.
標題:	Linear models (Statistics) -
電子資源:	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=104522An electronic book accessible through the World Wide Web; click for information
ISBN:	038722775X (electronic bk.)

Linear mixed models for longitudinal data
Verbeke, Geert.

Linear mixed models for longitudinal data[electronic resource] /Geert Verbeke, Geert Molenberghs. - New York :Springer,c2000. - xxii, 568 p. :ill. ;25 cm. - Springer series in statistics.

Includes bibliographical references (p. [523]-553) and index.

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.

Electronic reproduction.
Boulder, Colo. :
NetLibrary,
2004.

Available via World Wide Web.

ISBN: 038722775X (electronic bk.)Subjects--Topical Terms:

533190
Linear models (Statistics)
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA279 / .V458 2000eb

Dewey Class. No.: 519.5/3

Linear mixed models for longitudinal data
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300 $a xxii, 568 p. : $b ill. ; $c 25 cm.
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505 0 $a 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.
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