Statistical Methods For Handling Incomplete Data Av Jae Kwang Kim, Jun (department Of Statistics University Of Wisconsin Usa) Shao

<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> </P><B><P>Features</P><UL></B><P><LI>Uses the mean score equation as a building block for developing the theory for missing data analysis </LI><P></P><P><LI>Provides comprehensive coverage of computational techniques for missing data analysis </LI><P></P><P><LI>Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation </LI><P></P><P><LI>Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data </LI><P></P><P><LI>Describes a survey sampling application </LI><P></P><P><LI>Updated with a new chapter on Data Integration</LI><P></P><P><L

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