Statistical methods for handling incomplete data
Kim, Jae Kwang
Statistical methods for handling incomplete data - 2nd - London CRC Press 2022 - 364 p.
Table of Contents:
1. Introduction
2. Likelihood-based Approach
3. Computation
4. Imputation
5. Multiple Imputation
6. Fractional Imputation
7. Propensity Scoring Approach
8. Nonignorable Missing Data
9. Longitudinal and Clustered Data
10. Application to Survey Sampling
11. Data Integration
12. Advanced Topics
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.
Features
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
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.
(https://www.routledge.com/Statistical-Methods-for-Handling-Incomplete-Data/Kim-Shao/p/book/9781032118130)
9781032118130
Missing observations (Statistics)
Multiple imputation (Statistics)
Mathematics - Probability & Statistics - General
Statistical matching
519.54 / KIM
Statistical methods for handling incomplete data - 2nd - London CRC Press 2022 - 364 p.
Table of Contents:
1. Introduction
2. Likelihood-based Approach
3. Computation
4. Imputation
5. Multiple Imputation
6. Fractional Imputation
7. Propensity Scoring Approach
8. Nonignorable Missing Data
9. Longitudinal and Clustered Data
10. Application to Survey Sampling
11. Data Integration
12. Advanced Topics
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.
Features
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
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.
(https://www.routledge.com/Statistical-Methods-for-Handling-Incomplete-Data/Kim-Shao/p/book/9781032118130)
9781032118130
Missing observations (Statistics)
Multiple imputation (Statistics)
Mathematics - Probability & Statistics - General
Statistical matching
519.54 / KIM