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.

About the Author

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.

"About this title" may belong to another edition of this title.

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