Reduce Computation in Profile Empirical Likelihood Method

38 Pages Posted: 27 Apr 2013

See all articles by Minqiang Li

Minqiang Li

Bloomberg LP

Liang Peng

Georgia Institute of Technology

Yongcheng Qi

University of Minnesota - Duluth

Date Written: April 25, 2013

Abstract

Since its introduction by Owen, the empirical likelihood method has been extensively investigated and widely used to construct confidence regions and to test hypotheses in the literature. For a large class of statistics that can be obtained via solving estimating equations, the empirical likelihood function can be formulated from these estimating equations as proposed by Qin, J. and Lawless, J.F. (1994). If only a small part of parameters is of interest, a profile empirical likelihood method has to be employed to construct confidence regions, which could be computationally costly. In this paper we propose a jackknife empirical likelihood method to overcome this computational burden. This proposed method is easy to implement and works well in practice.

Keywords: profile empirical likelihood, estimating equation, Jackknife

JEL Classification: C10, C13

Suggested Citation

Li, Minqiang and Peng, Liang and Qi, Yongcheng, Reduce Computation in Profile Empirical Likelihood Method (April 25, 2013). Available at SSRN: https://ssrn.com/abstract=2256653 or http://dx.doi.org/10.2139/ssrn.2256653

Minqiang Li (Contact Author)

Bloomberg LP ( email )

731 Lexington Avenue
New York, NY 10022
United States

Liang Peng

Georgia Institute of Technology ( email )

Atlanta, GA 30332
United States

Yongcheng Qi

University of Minnesota - Duluth ( email )

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