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REDUCE COMPUTATION IN PROFILE EMPIRICAL LIKELIHOOD METHOD ∗

By Minqiang Li, Liang Peng and Yongcheng Qi

Abstract

Since its introduction by Owen in [29, 30], 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 [35]. 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. 1. Introduction. Empirica

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.189.4681
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