Statistical Methods for Handling Incomplete Data

Product Description 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.FeaturesUses 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 IntegrationNow 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.

Science & Math

Shao, Jun

Kim, Jae Kwang

Mathematics

Chapman and Hall/CRC

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. Suitable for graduate students and researchers in statistics, the book presents thorough treatments of: <ul>
<li>Statistical theories of likelihood-based inference with missing data</li> <li>Computational techniques and theories on imputation</li> <li>Methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching</li> </ul> Assuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.

Statistical Methods for Handling Incomplete Data (Chapman & Hall/CRC Texts in Statistical Science)

Reference

See the best price to sell, buy, or rent books by Kim, Jae Kwang. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Kim, Jae Kwang