Abstract Details
Activity Number:
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504
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Type:
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Invited
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract - #307048 |
Title:
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A Simulation Study on Bootstrap Variance Estimation of Sample Quantiles Under Doubly Protected Hot Deck Imputation
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Author(s):
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Hiroshi Saigo*+
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Companies:
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Waseda University
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Keywords:
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reimputation ;
adjustment ;
response model ;
imputation model
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Abstract:
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For randomly imputed survey data, two types of bootstrap variance estimation have been proposed: the adjustment approach where imputed values are adjusted to reflect the expectation of the imputed values in a bootstrap sample; and the reimputation approach where missing values are reimputed in a bootstrap sample. Both of them are known to provide consistent variance estimation if hot deck imputation yields approximately unbiased point estimation. In our simulation study, we apply the two approaches to variance estimation of sample quantiles under doubly protected hot deck imputation. Doubly protected hot deck imputation provides approximately unbiased point estimation if the response model or the imputation model is correct. Because to preserve the distribution of a variable under study is one of objectives in exercising hot deck imputation, variance estimation of sample quantiles is of practical importance. The simulation shows that the reimputation approach provides consistent variance estimation if either the response model or the imputation model is correct while the adjustment approach perfomrs poorly when the imputation model is incorrect.
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Authors who are presenting talks have a * after their name.
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