JSM 2004 - Toronto

Abstract #300131

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Activity Number: 140
Type: Invited
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #300131
Title: Inference Based on Nearest Neighbor Imputation and the Bootstrap
Author(s): Hansheng Wang*+ and Jun Shao
Companies: Peking University and University of Wisconsin
Address: Guanghua School of Management, Beijing, 100871, P.R. China
Keywords: confidence intervals ; hot-deck ; quantiles ; re-imputation ; smoothed bootstrap ; variance estimation
Abstract:

Nearest neighbor imputation (NNI) is a popular hot-deck imputation method used to compensate for item nonresponse in sample surveys. Although there exist results showing that NNI estimators are asymptotically unbiased and perform well in empirical studies, theoretical results for asymptotic inference (variance estimation and confidence intervals) are not available. We first establish the asymptotic normality of NNI estimators (such as the sample means and sample quantiles), which sets a foundation for large sample inference based on NNI. For variance estimation and confidence intervals, we consider a smoothed bootstrap method that addresses the effect of imputation by re-imputing nonrespondents in bootstrap datasets. The smoothed bootstrap method provides consistent variance estimators and asymptotically valid confidence intervals. We also carry out a simulation study to examine the finite sample performance of the smoothed bootstrap method.

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