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Cited by 1,077

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

A Review of Hot Deck Imputation for Survey Non‐response

Open access
Andridge, Rebecca R., Little, Roderick J. A.·2010·International Statistical Review

Abstract

Summary: Hot deck imputation is a method for handling missing data in which each missing value is replaced with an observed response from a “similar” unit. Despite being used extensively in practice, the theory is not as well developed as that of other imputation methods. We have found that no consensus exists as to the best way to apply the hot deck and obtain inferences from the completed data set. Here we review different forms of the hot deck and existing research on its statistical properties. We describe applications of the hot deck currently in use, including the U.S. Census Bureau's hot deck for the Current Population Survey (CPS). We also provide an extended example of variations of the hot deck applied to the third National Health and Nutrition Examination Survey (NHANES III). Some potential areas for future research are highlighted.

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Crossref · 10.1111/j.1751-5823.2010.00103.x

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Metadata

Authors
Andridge, R. R., & Little, R. J. A.
Venue
International Statistical Review
Publisher
Wiley
Published
2010-04
Volume
78
Issue
1
Pages
40-64
ISSN
  • 0306-7734 (ISSN-L)
  • 1751-5823
OpenAlex
W2162313689
Cited by
1,077
References
55
FWCI
23.70
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Review of Hot Deck Imputation for Survey Non‐response. https://app.cochat.ai/papers/10.1111/j.1751-5823.2010.00103.x
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Andridge, R. R., & Little, R. J. A. (2010). A Review of Hot Deck Imputation for Survey Non‐response. International Statistical Review. https://doi.org/10.1111/j.1751-5823.2010.00103.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.