Multiple Imputation for Nonresponse in Surveys

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John Wiley & Sons, Jun 9, 2004 - Mathematics - 287 pages
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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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Contents

INTRODUCTION
1
Problems
23
Probability Distributions and Related Calculations
31
Problems
68
UNDERLYING BAYESIAN THEORY
75
Scalar Q
77
The Posterior Cumulative Distribution Function of Q
83
The Conditional Distribution of Q Ux Given
89
SmallSample Monte Carlo Coverages of Asymptotically
135
Problems
148
PROCEDURES WITH IGNORABLE NONRESPONSE
154
Some Explicit Imputation Models with Univariate V
166
PROCEDURES WITH NON1GNORABLE
202
Formal Tasks with Nonignorable Nonresponse
210
5 The Imputation and Estimation Tasks with
214
Analysis of MultiplyImputed Data
221

The Conditional Distribution of Q Given Sm and B
94
5 A Situation in Which Conditional
105
RANDOMIZATIONBASED EVALUATIONS
113
1 If the CompleteData Inference
119
REFERENCES
244
AUTHOR INDEX
251
Report Written for the Census Bureau
268
Copyright

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Page 245 - An Overview of Hot-Deck Procedures." in Incomplete Data in Sample Surveys (Vol. 2): Theor\ ana
Page 247 - L (1983). Incomplete Data in Sample Surveys. Volume 3, Proceedings of the Symposium. New York: Academic Press. Madow, WG, Olkin. I., and Rubin. DB (1983). Incomplete Data in Sample Surveys, Volume 2, Theory and Bibliographies. New York: Academic Press, Marini, MM, Olsen.

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About the author (2004)

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.

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