English | 2021 | ISBN: 036728054X| 381 pages | pdf | 10.75 MB

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 Datacovers the most up-to-date statistical theories and computational methods for analyzing incomplete data.
Features
[list]
[*]Uses 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 Integration
[*]Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation
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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.
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