Statistical Analysis with Missing Data 3rd Edition by Roderick Little, Donald Rubin ISBN 1118595696 9781118595695

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ISBN 10: 1118595696

ISBN 13: 9781118595695

Author:  Roderick J. A. Little, Donald B. Rubin 

Statistical Analysis with Missing Data 3rd Edition: 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 data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems.

Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics.

• An updated “classic” written by renowned authorities on the subject
• Features over 150 exercises (including many new ones)
• Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods
• Revises previous topics based on past student feedback and class experience
• Contains an updated and expanded bibliography

The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work “has been no less than defining and transforming.” (ISI)

Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

 

Statistical Analysis with Missing Data 3rd Edition Table of contents:

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
   • 2.5 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
   • Problems

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
   • Problems

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
   • 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
   • 5.4 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 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
   • Problems
   • Notes

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
   • Problems

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
   • Problems

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
   • Problems

10. Bayes and Multiple Imputation

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

Part III: Likelihood-Based Approaches to the Analysis of Incomplete Data: Some Examples

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
• Problems

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
• Problems
• Notes

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
• Problems

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 Involving Mixtures of Continuous and Categorical Variables
• 14.5 Further Extensions of the General Location Model
• Problems

15. Missing Not at Random Models

• 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

 

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