Fractional hot deck imputation for robust inference under item nonresponse in survey sampling

Date
2014-01-01
Authors
Kim, Jae Kwang
Yang, Shu
Kim, Jae Kwang
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Altmetrics
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Statistics
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Statistics
Abstract

Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.

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This article is published as Kim, Jae Kwang, and Shu Yang. "Fractional hot deck imputation for robust inference under item nonresponse in survey sampling." Survey Methodology 40, no. 2 (2014): 211-230. Posted with permission.

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