Statistical Methods for Handling Incomplete Data

✍ Scribed by Jae Kwang Kim, Jun Shao

• Publisher: Chapman and Hall/CRC
• Year: 2021
• Tongue: English
• Leaves: 381
• Edition: 2
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
List of Figures
List of Tables
Preface
1. Introduction
1.1. Introduction
1.2. Outline
1.3. How to Use This Book
2. Likelihood-Based Approach
2.1. Introduction
2.2. Observed Likelihood
2.3. Mean Score Function
2.4. Observed Information
3. Computation
3.1. Introduction
3.2. Factoring Likelihood Approach
3.3. EM Algorithm
3.4. Monte Carlo Computation
3.5. Monte Carlo EM
3.6. Data Augmentation
4. Imputation
4.1. Introduction
4.2. Basic Theory
4.3. Variance Estimation after Imputation
4.4. Replication Variance Estimation
5. Multiple Imputation
5.1. Review of Bayesian Inference
5.2. MI: Bayesian Justification
5.3. MI: Frequentist Justification
5.4. MI Using Mixture Models
5.5. MI for General Purpose Estimation
6. Fractional Imputation
6.1. Parametric Fractional Imputation
6.2. Nonparametric Approach
6.3. Semiparametric Fractional Imputation
6.4. FI Using Mixture Models
6.5. FI for Multivariate Categorical Data
6.6. Model Selection
7. Propensity Scoring Approach
7.1. Introduction
7.2. Regression Weighting Method
7.3. Propensity Score Method
7.4. Optimal PSEstimation
7.5. Maximum Entropy Method
7.6. Doubly Robust Estimation
7.7. Empirical Likelihood Method
7.8. Nonparametric Method
8. Nonignorable Missing Data
8.1. Model Identification
8.2. Conditional Likelihood Approach
8.3. Pseudo Likelihood Approach
8.4. GMM Approach
8.5. Exponential Tilting Model
8.6. Latent Variable Approach
8.7. Callbacks
8.8. Capture-Recapture Experiment
9. Longitudinal and Clustered Data
9.1. Ignorable Missing Data
9.2. Nonignorable Monotone Missing Data
9.2.1. Parametric Models
9.2.2. Nonparametric p(y | x)
9.2.3. Nonparametric Propensity
9.3. Past-Value-Dependent Missing Data
9.3.1. Three Different Approaches
9.3.2. Imputation Models under Past-Value-Dependent Nonmonotone Missing
9.3.3. Nonparametric Regression Imputation
9.3.4. Dimension Reduction
9.3.5. Simulation Study
9.3.6. Wisconsin Diabetes Registry Study
9.4. Random-Effect-Dependent Missing Data
9.4.1. Three Existing Approaches
9.4.2. Summary Statistics
9.4.3. Simulation Study
9.4.4. Modification of Diet in Renal Disease
10. Application to Survey Sampling
10.1. Introduction
10.2. Calibration Estimation
10.3. Propensity Score Weighting Method
10.4. Multiple Imputation
10.5. Fractional Imputation
10.6. Fractional Hot Deck Imputation
10.7. Imputation for Two-Phase Sampling
10.8. Synthetic Data Imputation
11. Data Integration
11.1. Mass Imputation
11.2. Propensity Score Method
11.3. Nonparametric Propensity Score Approach
11.4. Doubly Robust Method
11.5. Statistical Matching
11.6. Mass Imputation Using Multilevel Models
11.7. Data Integration for Regression Analysis
11.8. Record Linkage
12. Advanced Topics
12.1. Smoothing Spline Imputation
12.2. Kernel Ridge Regression Imputation
12.3. KRR-Based Propensity Score Estimation
12.4. Soft Calibration
12.5. Penalized Regression Imputation
12.6. Sufficient Dimension Reduction
12.7. Neural Network Model
Bibliography
Index