The Annals of Statistics

Empirical Likelihood in Biased Sample Problems

Jing Qin

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Abstract

It is well known that we can use the likelihood ratio statistic to test hypotheses and to construct confidence intervals in full parametric models. Recently, Owen introduced the empirical likelihood method in nonparametric models. In this paper, we generalize his results to biased sample problems. A Wilks theorem leading to a likelihood ratio confidence interval for the mean is given. Some extensions, discussion and simulations are presented.

Article information

Source
Ann. Statist., Volume 21, Number 3 (1993), 1182-1196.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349257

Digital Object Identifier
doi:10.1214/aos/1176349257

Mathematical Reviews number (MathSciNet)
MR1241264

Zentralblatt MATH identifier
0791.62052

JSTOR
links.jstor.org

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62D05: Sampling theory, sample surveys

Keywords
Biased sample empirical likelihood test of hypotheses $M$-estimator Wilks' theorem

Citation

Qin, Jing. Empirical Likelihood in Biased Sample Problems. Ann. Statist. 21 (1993), no. 3, 1182--1196. doi:10.1214/aos/1176349257. https://projecteuclid.org/euclid.aos/1176349257


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