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Preface to the Third Edition xi
Part I Overview and Basic Approaches 1
Chapter 1. Introduction 3
Chapter 2. Missing Data in Experiments 29
Chapter 3. Complete-Case and Available-Case Analysis, Including Weighting Methods 47
Chapter 4. Single Imputation Methods 67
Chapter 5. Accounting for Uncertainty from Missing Data 85
Part II Likelihood-Based Approaches to the Analysis of Data with Missing Values 107
Chapter 6. Theory of Inference Based on the Likelihood Function 109
Chapter 7. Factored Likelihood Methods When the Missingness Mechanism Is Ignorable 151
Chapter 8. Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse 185
Chapter 9. Large-Sample Inference Based on Maximum Likelihood Estimates 213
Chapter 10. Bayes and Multiple Imputation 223
Part III Likelihood-Based Approaches to the Analysis of Incomplete Data: Some Examples 247
Chapter 11. Multivariate Normal Examples, Ignoring the Missingness Mechanism 249
Chapter 12. Models for Robust Estimation 285
Chapter 13. Models for Partially Classified Contingency Tables, Ignoring the Missingness Mechanism 301
Chapter 14. Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missingness Mechanism 329
Chapter 15. Missing Not at RandomModels 351
References 405
Author Index 429
Subject Index 437
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