Statistical analysis with missing data / Roderick J.A. Little, Donald B. Rubin.

Includes index.

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Intro; Statistical Analysis with Missing Data; Contents; Preface to the Third Edition; Part I Overview and Basic Approaches; 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; Problems; Note; 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; 2.4.1 Yatess Method; 2.4.2 Using a Formula for the Missing Values

2.4.3 Iterating to Find the Missing Values2.4.4 ANCOVA with Missing Value Covariates; 2.5 Bartletts ANCOVA Method; 2.5.1 Useful Properties of Bartletts Method; 2.5.2 Notation; 2.5.3 The ANCOVA Estimates of Parameters and Missing Y-Values; 2.5.4 ANCOVA Estimates of the Residual Sums of Squares and the Covariance Matrix of; 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

Problems3 Complete-Case and Available-Case Analysis, Including Weighting Methods; 3.1 Introduction; 3.2 Complete-Case Analysis; 3.3 Weighted Complete-Case Analysis; 3.3.1 Weighting Adjustments; 3.3.2 Poststratification and Raking to Known Margins; 3.3.3 Inference from Weighted Data; 3.3.4 Summary of Weighting Methods; 3.4 Available-Case Analysis; Problems; 4 Single Imputation Methods; 4.1 Introduction; 4.2 Imputing Means from a Predictive Distribution; 4.2.1 Unconditional Mean Imputation; 4.2.2 Conditional Mean Imputation; 4.3 Imputing Draws from a Predictive Distribution

4.3.1 Draws Based on Explicit Models4.3.2 Draws Based on Implicit Models-Hot Deck Methods; 4.4 Conclusion; Problems; 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; 5.3.1 Bootstrap Standard Errors; 5.3.2 Jackknife Standard Errors; 5.4 Introduction to Multiple Imputation; 5.5 Comparison of Resampling Methods and Multiple Imputation; Problems; Part II Likelihood-Based Approaches to the Analysis of Data with Missing Values

6 Theory of Inference Based on the Likelihood Function6.1 Review of Likelihood-Based Estimation for Complete Data; 6.1.1 Maximum Likelihood Estimation; 6.1.2 Inference Based on the Likelihood; 6.1.3 Large Sample Maximum Likelihood and Bayes Inference; 6.1.4 Bayes Inference Based on the Full Posterior Distribution; 6.1.5 Simulating Posterior Distributions; 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; 6.3.1 The Method; 6.3.2 Background; 6.3.3 Examples