Population empirical likelihood for nonparametric inference in survey sampling
Date
2014-01-01
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Person
Kim, Jae Kwang
Professor
Research Projects
Organizational Units
Organizational Unit
Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
Journal Issue
Is Version Of
Versions
Series
Department
Statistics
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.
Description
Keywords
Citation
DOI
Copyright
Wed Jan 01 00:00:00 UTC 2014