Statistical analysis with missing data /
AN UP-TO-DATE, COMPREHENSIVE TREATMENT OF A CLASSIC TEXT ON MISSING DATA IN STATISTICS The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
Wiley,
2020.
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| Edition: | Third edition. |
| Series: | Wiley series in probability and statistics
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- 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
