Statistical Analysis with Missing Data

Chapter 1: Introduction

• • 1.1 The Problem of Missing Data
• • 1.2 Missingness Patterns and Mechanisms
• • 1.3 Mechanisms That Lead to Missing Data
• • 1.4 A Taxonomy of Missing Data Methods

Chapter 2: Missing Data in Experiments

• • 2.1 Introduction
• • 2.2 The Exact Least Squares Solution with Complete Data
• • 2.3 The Correct Least Squares Analysis with Missing Data
• • 2.4 Filling in Least Squares Estimates
• • 2.5 Bartlett’s ANCOVA Method
• • 2.6 Least Squares Estimates of Missing Values by ANCOVA Using Only Complete-Data Methods
• • 2.7 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares
• • 2.8 Correct Least-Squares Sums of Squares with More Than One Degree of Freedom

Chapter 3: Complete-Case and Available-Case Analysis, Including Weighting Methods

• • 3.1 Introduction
• • 3.2 Complete-Case Analysis
• • 3.3 Weighted Complete-Case Analysis
• • 3.4 Available-Case Analysis

Chapter 4: Single Imputation Methods

• • 4.1 Introduction
• • 4.2 Imputing Means from a Predictive Distribution
• • 4.3 Imputing Draws from a Predictive Distribution
• • 4.4 Conclusion

Chapter 5: Accounting for Uncertainty from Missing Data

• • 5.1 Introduction
• • 5.2 Imputation Methods that Provide Valid Standard Errors from a Single Filled-in Data Set
• • 5.3 Standard Errors for Imputed Data by Resampling
• • 5.4 Introduction to Multiple Imputation
• • 5.5 Comparison of Resampling Methods and Multiple Imputation

Chapter 6: Theory of Inference Based on the Likelihood Function

• • 6.1 Review of Likelihood-Based Estimation for Complete Data
• • 6.2 Likelihood-Based Inference with Incomplete Data
• • 6.3 A Generally Flawed Alternative to Maximum Likelihood: Maximizing over the Parameters and the Missing Data
• • 6.4 Likelihood Theory for Coarsened Data

Chapter 7: Factored Likelihood Methods When the Missingness Mechanism Is Ignorable

• • 7.1 Introduction
• • 7.2 Bivariate Normal Data with One Variable Subject to Missingness: ML Estimation
• • 7.3 Bivariate Normal Monotone Data: Small-Sample Inference
• • 7.4 Monotone Missingness with More Than Two Variables
• • 7.5 Factored Likelihoods for Special Nonmonotone Patterns

Chapter 8: Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse

• • 8.1 Alternative Computational Strategies
• • 8.2 Introduction to the EM Algorithm
• • 8.3 The E Step and The M Step of EM
• • 8.4 Theory of the EM Algorithm
• • 8.5 Extensions of EM
• • 8.6 Hybrid Maximization Methods

Chapter 9: Large-Sample Inference Based on Maximum Likelihood Estimates

• • 9.1 Standard Errors Based on The Information Matrix
• • 9.2 Standard Errors via Other Methods

Chapter 10: Bayes and Multiple Imputation

• • 10.1 Bayesian Iterative Simulation Methods
• • 10.2 Multiple Imputation

Chapter 11: Multivariate Normal Examples, Ignoring the Missingness Mechanism

• • 11.1 Introduction
• • 11.2 Inference for a Mean Vector and Covariance Matrix with Missing Data Under Normality
• • 11.3 The Normal Model with a Restricted Covariance Matrix
• • 11.4 Multiple Linear Regression
• • 11.5 A General Repeated-Measures Model with Missing Data
• • 11.6 Time Series Models
• • 11.7 Measurement Error Formulated as Missing Data

Chapter 12: Models for Robust Estimation

• • 12.1 Introduction
• • 12.2 Reducing the Influence of Outliers by Replacing the Normal Distribution by a Longer-Tailed Distribution
• • 12.3 Penalized Spline of Propensity Prediction

Chapter 13: Models for Partially Classified Contingency Tables, Ignoring the Missingness Mechanism

• • 13.1 Introduction
• • 13.2 Factored Likelihoods for Monotone Multinomial Data
• • 13.3 ML and Bayes Estimation for Multinomial Samples with General Patterns of Missingness
• • 13.4 Loglinear Models for Partially Classified Contingency Tables

Chapter 14: Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missingness Mechanism

• • 14.1 Introduction
• • 14.2 The General Location Model
• • 14.3 The General Location Model with Parameter Constraints
• • 14.4 Regression Problems InvolvingMixtures of Continuous and Categorical Variables
• • 14.5 Further Extensions of the General Location Model

Chapter 15: Missing Not at RandomModels

• • 15.1 Introduction
• • 15.2 Models with Known MNAR Missingness Mechanisms: Grouped and Rounded Data
• • 15.3 Normal Models for MNAR Missing Data
• • 15.4 Other Models and Methods for MNAR Missing Data