Statistical Methods for Handling Incomplete Data

Description

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. 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