Rubin Multiple Imputation for Nonresponse in Surveys

99. Auflage 2009
ISBN: 978-0-470-31736-5
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 320 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

Autoren/Hrsg.

Rubin, Donald B.

Inhaltsverzeichnis

Tables and Figures.
Glossary.
1. Introduction.
1.1 Overview.
1.2 Examples of Surveys with Nonresponse.
1.3 Properly Handling Nonresponse.
1.4 Single Imputation.
1.5 Multiple Imputation.
1.6 Numerical Example Using Multiple Imputation.
1.7 Guidance for the Reader.
2. Statistical Background.
2.1 Introduction.
2.2 Variables in the Finite Population.
2.3 Probability Distributions and Related Calculations.
2.4 Probability Specifications for Indicator Variables.
2.5 Probability Specifications for (X,Y).
2.6 Bayesian Inference for a Population Quality.
2.7 Interval Estimation.
2.8 Bayesian Procedures for Constructing Interval Estimates,Including Significance Levels and Point Estimates.
2.9 Evaluating the Performance of Procedures.
2.10 Similarity of Bayesian and Randomization-Based Inferencesin Many Practical Cases.
3. Underlying Bayesian Theory.
3.1 Introduction and Summary of Repeated-ImputationInferences.
3.2 Key Results for Analysis When the Multiple Imputations areRepeated Draws from the Posterior Distribution of the MissingValues.
3.3 Inference for Scalar Estimands from a Modest Number ofRepeated Completed-Data Means and Variances.
3.4 Significance Levels for Multicomponent Estimands from aModest Number of Repeated Completed-Data Means andVariance-Covariance Matrices.
3.5 Significance Levels from Repeated Completed-DataSignificance Levels.
3.6 Relating the Completed-Data and Completed-Data PosteriorDistributions When the Sampling Mechanism is Ignorable.
4. Randomization-Based Evaluations.
4.1 Introduction.
4.2 General Conditions for the Randomization-Validity ofInfinite-m Repeated-Imputation Inferences.
4.3Examples of Proper and Improper Imputation Methods in aSimple Case with Ignorable Nonresponse.
4.4 Further Discussion of Proper Imputation Methods.
4.5 The Asymptotic Distibution of(&Qmacr;m,Ūm,Bm)for Proper Imputation Methods.
4.6 Evaluations of Finite-m Inferences with ScalarEstimands.
4.7 Evaluation of Significance Levels from the Moment-BasedStatistics Dm and &Dtilde;m withMulticomponent Estimands.
4.8 Evaluation of Significance Levels Based on RepeatedSignificance Levels.
5. Procedures with Ignorable Nonresponse.
5.1 Introduction.
5.2 Creating Imputed Values under an Explicit Model.
5.3 Some Explicit Imputation Models with UnivariateYI and Covariates.
5.4 Monotone Patterns of Missingness in MultivariateYI.
5.5 Missing Social Security Benefits in the Current PopulationSurvey.
5.6 Beyond Monotone Missingness.
6. Procedures with Nonignorable Nonresponse.
6.1 Introduction.
6.2 Nonignorable Nonresponse with UnivariateYI and No XI.
6.3 Formal Tasks with Nonignorable Nonresponse.
6.4 Illustrating Mixture Modeling Using Educational TestingService Data.
6.5 Illustrating Selection Modeling Using CPS Data.
6.6 Extensions to Surveys with Follow-Ups.
6.7 Follow-Up Response in a Survey of Drinking Behavior AmongMen of Retirement Age.
References.
Author Index.
Subject Index.
Appendix I. Report Written for the Social SecurityAdministration in 1977.
Appendix II. Report Written for the Census Bureau in 1983.

Über Autor(innen)

Donald B. Rubin , PhD, is a John L. Loeb Professor of Statistics at Harvard University in Cambridge, MA. He was named 2000-2001 Statistician of the Year by the Chicago Chapter of ASA. His research interests include causal inference in experiments and observational studies, developing and applying statistical models to data in a variety of scientific disciplines, and the application of Bayesian and empirical Bayesian techniques.