Statistical methods for handling incomplete data

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

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. (https://www.routledge.com/Statistical-Methods-for-Handling-Incomplete-Data/Kim-Shao/p/book/9781032118130)