Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.
Features
- Uses the mean score equation as a building block for developing the theory for missing data analysis
- Provides comprehensive coverage of computational techniques for missing data analysis
- Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation
- Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data
- Describes a survey sampling application
- Updated with a new chapter on Data Integration
