Yulei He, Guangyu Zhang, Chiu-Hsieh Hsu

Multiple Imputation of Missing Data in Practice (eBook, PDF)

Basic Theory and Analysis Strategies

• Format: PDF

Produktbeschreibung

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 494
• Erscheinungstermin: 19. November 2021
• Englisch
• ISBN-13: 9781498722070
• Artikelnr.: 62613516

Autorenporträt

Yulei He and Guangyu Zhang are mathematical statisticians at the National Center for Health Statistics, the U.S. Centers for Disease Control and Prevention. Chiu-Heish Hsu is a Professor of Biostatistics at the University of Arizona. All authors have researched, taught, and consulted in multiple imputation and missing data analysis in the past 20 years.

Inhaltsangabe

1. Introduction. 2. Statistical Background. 3. Multiple Imputation Analysis: Basics. 4. Multiple Imputation for Univariate Missing Data: Parametric Methods. 5. Multiple Imputation for Univariate Missing Data: Robust Methods. 6. Multiple Imputation for Multivariate Missing Data: the Joint Modeling Approach. 7. Multiple Imputation for Multivariate Missing Data: the Fully Conditional Specification Approach. 8. Multiple Imputation in Survival Data Analysis. 9. Multiple Imputation for Longitudinal Data. 10. Multiple Imputation Analysis for Complex Survey Data. 11. Multiple Imputation for Data Subject to Measurement Error. 12. Multiple Imputation Diagnostics.

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1. Introduction. 2. Statistical Background. 3. Multiple Imputation
Analysis: Basics. 4. Multiple Imputation for Univariate Missing Data:
Parametric Methods. 5. Multiple Imputation for Univariate Missing Data:
Robust Methods. 6. Multiple Imputation for Multivariate Missing Data: the
Joint Modeling Approach. 7. Multiple Imputation for Multivariate Missing
Data: the Fully Conditional Specification Approach. 8. Multiple Imputation
in Survival Data Analysis. 9. Multiple Imputation for Longitudinal Data.
10. Multiple Imputation Analysis for Complex Survey Data. 11. Multiple
Imputation for Data Subject to Measurement Error. 12. Multiple Imputation
Diagnostics.