Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2014

Journal or Book Title

Statistica Sinica

Volume

24

First Page

335

Last Page

355

DOI

10.5705/ss.2011.294

Abstract

Empirical likelihood is a popular tool for incorporating auxiliary information and constructing nonparametric confidence intervals. In survey sampling, sample elements are often selected by using an unequal probability sampling method and the empirical likelihood function needs to be modified to account for the unequal probability sampling. Wu and Rao (2006) proposed a way of constructing confidence regions using the pseudo empirical likelihood of Chen and Sitter (1999).
In this paper, we propose using empirical likelihood in survey sampling based on the so-called population empirical likelihood (POEL). In the POEL approach, a single empirical likelihood is defined for the finite population. The sampling design can be incorporated into the constraint in the optimization of the POEL. For some special sampling designs, the proposed method leads to optimal estimation and does not require artificial adjustment for constructing likelihood ratio confidence intervals. Furthermore, because a single empirical likelihood is defined for the finite population, it naturally incorporates auxiliary information obtained from multiple surveys. Results from two simulation studies are presented to show the finite sample performance of the proposed method.

Comments

This article is published as Chen, S. and Kim, J.K. (2014). “Population empirical likelihood for nonparametric inference in survey sampling,” Statistica Sinica 24, 335–355. doi:10.5705/ss.2011.294. Posted with permission.

Copyright Owner

Academia Sinica

Language

en

File Format

application/pdf

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