This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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483
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #309291 |
Title:
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Bootstrap Variance Estimation in the Presence of Imputed Data
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Author(s):
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Christian Léger*+ and Zeinab Mashreghi and David Haziza
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Companies:
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Université de Montréal and Université de Montréal and Université de Montréal
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Address:
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Département de mathématiques et de statistique, Montréal, QC, H3C 3J7, Canada
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Keywords:
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variance estimation ;
bootstrap ;
imputation ;
sampling weights
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Abstract:
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In the presence of imputed data, Shao & Sitter (1996) introduced a bootstrap method where the non-respondents in the bootstrap sample are reimputed with the same method, which leads to a valid variance estimator if the sampling fraction is small. We argue that this method can be justified by the reverse framework for variance estimation developed by Shao and Steel (1999). When the sampling fraction is small, this framework suggests a second valid bootstrap variance estimator that can be obtained using complete data bootstrap methods (e.g., the method of Rao, Wu and Yue, 1992) and does not require reimputing. Finally, we obtain a third variance estimator, valid even when the sampling fraction is not small, by modifying the weighting to account for non-response in the bootstrap weights approach and resampling the data and the response status independently rather than jointly.
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