Donald B. Rubin

Multiple Imputation for Nonresponse in Surveys (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 320
• Erscheinungstermin: 4. November 2009
• Englisch
• ISBN-13: 9780470317365
• Artikelnr.: 38193854

Autorenporträt

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.

Inhaltsangabe

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 Inferences in Many
Practical Cases. 3. Underlying Bayesian Theory. 3.1 Introduction and
Summary of Repeated-Imputation Inferences. 3.2 Key Results for Analysis
When the Multiple Imputations are Repeated Draws from the Posterior
Distribution of the Missing Values. 3.3 Inference for Scalar Estimands from
a Modest Number of Repeated Completed-Data Means and Variances. 3.4
Significance Levels for Multicomponent Estimands from a Modest Number of
Repeated Completed-Data Means and Variance-Covariance Matrices. 3.5
Significance Levels from Repeated Completed-Data Significance Levels. 3.6
Relating the Completed-Data and Completed-Data Posterior Distributions When
the Sampling Mechanism is Ignorable. 4. Randomization-Based Evaluations.
4.1 Introduction. 4.2 General Conditions for the Randomization-Validity of
Infinite-m Repeated-Imputation Inferences. 4.3Examples of Proper and
Improper Imputation Methods in a Simple Case with Ignorable Nonresponse.
4.4 Further Discussion of Proper Imputation Methods. 4.5 The Asymptotic
Distibution of (Q_m,U_m,B_m) for Proper Imputation Methods. 4.6 Evaluations
of Finite-m Inferences with Scalar Estimands. 4.7 Evaluation of
Significance Levels from the Moment-Based Statistics D_m and D~_m with
Multicomponent Estimands. 4.8 Evaluation of Significance Levels Based on
Repeated Significance 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 Univariate Y_I and Covariates. 5.4 Monotone
Patterns of Missingness in Multivariate Y_I. 5.5 Missing Social Security
Benefits in the Current Population Survey. 5.6 Beyond Monotone Missingness.
6. Procedures with Nonignorable Nonresponse. 6.1 Introduction. 6.2
Nonignorable Nonresponse with Univariate Y_I and No X_I. 6.3 Formal Tasks
with Nonignorable Nonresponse. 6.4 Illustrating Mixture Modeling Using
Educational Testing Service 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 Among Men of Retirement Age. References.
Author Index. Subject Index. Appendix I. Report Written for the Social
Security Administration in 1977. Appendix II. Report Written for the Census
Bureau in 1983.