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Article
Semiparametric fractional imputation using empirical likelihood in survey sampling
Statistical Theory and Related Fields
  • Sixia Chen, University of Oklahoma
  • Jae Kwang Kim, Iowa State University
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
6-1-2017
DOI
10.1080/24754269.2017.1328244
Abstract

The empirical likelihood method is a powerful tool for incorporating moment conditions in statistical inference. We propose a novel application of the empirical likelihood for handling item nonresponse in survey sampling. The proposed method takes the form of fractional imputation (Kim, 2011) but it does not require parametric model assumptions. Instead, only the first moment condition based on a regression model is assumed and the empirical likelihood method is applied to the observed residuals to get the fractional weights. The resulting semiparametric fractional imputation provides -consistent estimates for various parameters. Variance estimation is implemented using a jackknife method. Two limited simulation studies are presented to compare several imputation estimators.

Comments

This is a manuscript of an article published as Chen, Sixia, and Jae kwang Kim. "Semiparametric fractional imputation using empirical likelihood in survey sampling." Statistical theory and related fields 1, no. 1 (2017): 69-81. doi: 10.1080/24754269.2017.1328244. Posted with permission.

Copyright Owner
East China Normal University
Language
en
File Format
application/pdf
Citation Information
Sixia Chen and Jae Kwang Kim. "Semiparametric fractional imputation using empirical likelihood in survey sampling" Statistical Theory and Related Fields Vol. 1 Iss. 1 (2017) p. 69 - 81
Available at: http://works.bepress.com/jae-kwang-kim/45/