Statistical methods for handling incomplete data With the advances in statistical computing there has been a rapid development of techniques and applications in missing data analysis. This book aims to cover the most up to date statistical theories and computational methods for analyzing incomplete data through (1)vigorous treatment of statistical theories on likelihood based inference with missing data (2) comprehensive treatment of computational techniques and theories on imputation and (3) most up to date treatment of methodologies involving propensity score weighting nonignorable missing longitudinal missing survey sampling application and statistical matching. The book is suitable for use as a textbook for a graduate course in statistics departments or as a reference book for those interested in this area. Some of the research ideas introduced in the book can be developed further for specific applications