Reduce computation in profile empirical likelihood method |
| |
Authors: | Minqiang Li Liang Peng Yongcheng Qi |
| |
Affiliation: | 1. Bloomberg LP, 731 Lexington Avenue, New York, NY 10022, USA;2. School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA;3. Department of Mathematics and Statistics, University of Minnesota–Duluth, Duluth, MN 55812, USA |
| |
Abstract: | Since its introduction by Owen (1988, 1990), 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 and Lawless (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 article the authors propose a jackknife empirical likelihood method to overcome this computational burden. This proposed method is easy to implement and works well in practice. The Canadian Journal of Statistics 39: 370–384; 2011 © 2011 Statistical Society of Canada |
| |
Keywords: | Estimating equation jackknife profile empirical likelihood Primary 62E20 secondary 62F12 |
|
|