Jae Kwang Kim – författare

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.

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.

Statistics in Survey Sampling offers a comprehensive and rigorous introduction to the principles and practices of survey sampling. Bridging the gap between statistical theory and real-world data collection, this textbook presents both classical methods and modern developments, equipping readers with the tools to design effective surveys and make reliable inferences from sample data.With a strong foundation in design-based inference and frequentist methodology, the book emphasizes representativeness, efficiency, and the integration of auxiliary information in estimation procedures. It also introduces emerging research topics that reflect the evolving landscape of data collection and analysis.Key Features:Rigorous treatment of statistical theory for design-based inference in probability samplingThorough exploration of model-assisted estimation techniques using auxiliary dataCoverage of modern topics including data integration, analytic inference, predictive inference, and voluntary sample analysisDetailed examples illustrate the methods throughout the bookFocused development within the frequentist framework, with limited emphasis on Bayesian or nonparametric methodsExercises in all chapters enable use as a course text or for self-studyIncludes appendices on key background topics such as asymptotic theory and projection techniquesThis textbook is ideal for graduate students in statistics with prior courses in statistical theory and linear models. It is also a valuable reference for researchers and practitioners engaged in survey design, public policy evaluation, official statistics, and data science applications involving sample-based inference.