Statistical Analysis with Missing Data - Little, Roderick J. A.

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Auteur(s) : Little, Roderick J. A. - Rubin, Donald B.

Editeur : Wiley John + Sons

Langue : Anglais

Parution : 30/04/2019

Format : Moyen, de 350g à 1kg

Nombre de pages : 462

Expédition : 817

Dimensions : 23.5 x 15.7 x 2.9

ISBN : 9780470526798

Résumé :
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....

Biographie:

Roderick J. A. Little, PhD., is Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan.

Donald B. Rubin, PhD., is Professor, Yau Mathematical Sciences Center, Tsinghua University...

Sommaire:

Preface to the Third Edition xi

Part I Overview and Basic Approaches 1

1 Introduction 3

1.1 The Problem of Missing Data 3

1.2 Missingness Patterns and Mechanisms 8

1.3 Mechanisms That Lead to Missing Data 13

1.4 A Taxonomy of Missing Data Methods 23

2 Missing Data in Experiments 29

2.1 Introduction 29

2.2 The Exact Least Squares Solution with Complete Data 30

2.3 The Correct Least Squares Analysis with Missing Data 32

2.4 Filling in Least Squares Estimates 33

2.4.1 Yates's Method 33

2.4.2 Using a Formula for the Missing Values 34

2.4.3 Iterating to Find the Missing Values 34

2.4.4 ANCOVA with Missing Value Covariates 35

2.5 Bartlett's ANCOVA Method 35

2.5.1 Useful Properties of Bartlett's Method 35

2.5.2 Notation 36

2.5.3 The ANCOVA Estimates of Parameters and Missing Y-Values 36

2.5.4 ANCOVA Estimates of the Residual Sums of Squares and the Covariance Matrix of ?? 37

2.6 Least Squares Estimates of Missing Values by ANCOVA Using Only Complete-Data Methods 38

2.7 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares 40

2.8 Correct Least-Squares Sums of Squares with More Than One Degree of Freedom 42

3 Complete-Case and Available-Case Analysis, Including Weighting Methods 47

3.1 Introduction 47

3.2 Complete-Case Analysis 47

3.3 Weighted Complete-Case Analysis 50

3.3.1 Weighting Adjustments 50

3.3.2 Poststratification and Raking to Known Margins 58

3.3.3 Inference from Weighted Data 60

3.3.4 Summary of Weighting Methods 61

3.4 Available-Case Analysis 61

4 Single Imputation Methods 67

4.1 Introduction 67

4.2 Imputing Means from a Predictive Distribution 69

4.2.1 Unconditional Mean Imputation 69

4.2.2 Conditional Mean Imputation 70

4.3 Imputing Draws from a Predictive Distribution 73

4.3.1 Draws Based on Explicit Models 73

4.3.2 Draws Based on Implicit Models - Hot Deck Methods 76

4.4 Conclusion 81

5 Accounting for Uncertainty from Missing Data 85

5.1 Introduction 85

5.2 Imputation Methods that Provide Valid Standard Errors from a Single Filled-in Data Set 86

5.3 Standard Errors for Imputed Data by Resampling 90

5.3.1 Bootstrap Standard Errors 90

5.3.2 Jackknife Standard Errors 92

5.4 Introduction to Multiple Imputation 95

5.5 Comparison of Resampling Methods and Multiple Imputation 100

Part II Likelihood-Based Approaches to the Analysis of Data with Missing Values 107

6 Theory of Inference Based on the Likelihood Function 109

6.1 Review of Likelihood-Based Estimation for Complete Data 109

6.1.1 Maximum Likelihood Estimation 109

6.1.2 Inference Based on the Likelihood 118

6.1.3 Large Sample Maximum Likelihood and Bayes Inference 119

6.1.4 Bayes Inference Based on the Full Posterior Distribution 126

6.1.5 Simulating Posterior Distributions 130

6.2 Likelihood-Based Inference with Incomplete Data 132

6.3 A Generally Flawed Alternative to Maximum Likelihood: Maximizing over the Parameters and the Missing Data 141

6.3.1 The Method 141

6.3.2 Background 142

6.3.3 Examples 143

6.4 Likelihood Theory for Coarsened Data 145

7 Factored Likelihood Methods When the Missingness Mechanism Is Ignorable 151

7.1 Introduction 151

7.2 Bivariate Normal Data with One Variable Subject to Missingness: ML Estimation 153

7.2.1 ML Estimates 153

7.2.2 Large-Sample Covariance Matrix 157

7...