Jae-Kwang Kim, LAS Dean's Professor in the Department of Statistics, published the second edition of his book, "Statistical Methods for Handling Incomplete Data." Pictured to the right is Professor Kim with LAS Dean Beatte Schmittman receiving his LAS Award for Outstanding Achievement in Research in Statistics.
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
- Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation
The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.
Table of Contents
1. Introduction
2. Likelihood-based Approach
3. Computation
4. Imputation
5. Multiple Imputation
6. Fractional Imputation
7. Propensity Scoring Approach
8. Nonignorable Missing Data
9. Longitudinal and Clustered Data
10. Application to Survey Sampling
11. Data Integration
12. Advanced Topics