Statistical Methods for Handling Incomplete Data

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. This book covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Autorentext

Jae Kwang Kim is a LAS dean's professor in the Department of Statistics at Iowa State University. He is a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He is the recipient of 2015 Gertude M. Cox award, sponsored by Washington Statistical Society and RTI international.

Jun Shao is a professor in the Department of Statistics at University of Wisconsin Madison. He is a fellow of ASA and IMS, a former president of International Chinese Statistical Association and currently the founding editor of Statistical Theory and Related Fields.

Inhalt

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