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 analysisProvides comprehensive coverage of computational techniques for missing data analysisPresents a rigorous treatment of imputation techniques, including multiple imputation fractional imputationExplores the most recent advances of the propensity score method and estimation techniques for nonignorable missing dataDescribes a survey sampling applicationUpdated with a new chapter on Data IntegrationNow includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputationThe 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.