Empirical likelihood-based hot deck imputation methods
Yijie Xue and
Nicole Lazar
Journal of Nonparametric Statistics, 2012, vol. 24, issue 3, 629-646
Abstract:
The non-response problem commonly exists in survey data and has been investigated by various methods. We propose an empirical likelihood-based hot deck imputation method, which resamples the observed data by using the weights from the empirical likelihood ratio for missing values. We demonstrate that the estimator of the mean is unbiased and the corresponding variance estimator of the mean is asymptotically unbiased under mild conditions. Next, we extend our method for U-statistic estimators and show that the estimator converges to the real U-statistic in probability. The proposed method can also incorporate multiple imputations and/or regression imputations easily. Simulations and a real example illustrate that our method outperforms some of the existing approaches, such as simple hot deck imputation and fractional hot deck imputation. We conclude with a discussion of the advantages of the empirical likelihood-based hot deck imputation method.
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2012.690879 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:24:y:2012:i:3:p:629-646
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485252.2012.690879
Access Statistics for this article
Journal of Nonparametric Statistics is currently edited by Jun Shao
More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().