Statistical Methods for Handling Incomplete Data

<p>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. <b>Statistical Methods for Handling Incomplete Data</b> covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.</p><p>Features</p><ul> <p> </p> <li>Uses the mean score equation as a building block for developing the theory for missing data analysis </li> <p> </p> <li>Provides comprehensive coverage of computational techniques for missing data analysis </li> <p> </p> <li>Presents a rigorous treatment of imputation techniques including multiple imputation fractional imputation </li> <p> </p> <li>Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data </li> <p> </p> <li>Describes a survey sampling application </li> <p> </p> <li>Updated with a new chapter on Data Integration</li> <p> </p> <li>Now includes a chapter on Advanced Topics including kernel ridge regression imputation and neural network model imputation </li> </ul><p>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.</p>