EXTENDING THE SCOPE OF EMPIRICAL LIKELIHOOD

• 成果类型: Article
• 署名作者: Hjort, Nils Lid; McKeague, Ian W.; Van Keilegom, Ingrid
• 署名单位: University of Oslo; Columbia University; Universite Catholique Louvain; Tilburg University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/07-AOS555
• 发表日期: 2009
• 页码: 1079-1111
• 关键词: strong uniform consistency; confidence-intervals; density; models; limit; CONVERGENCE; inference; variance; number; weak

摘要

This article extends the scope of empirical likelihood methodology ill three directions: to allow for plug-in estimates Of nuisance parameters in estimating equations, slower than root n-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size. Calibrating empirical likelihood confidence regions with plug-in is sometimes intractable due to the complexity of the asymptotics, so we introduce a bootstrap approximation that call be used in such situations. We provide a range of examples from survival analysis and nonparametric statistics to illustrate the main results.