Jackknife Empirical Likelihood

• 成果类型: Article
• 署名作者: Jing, Bing-Yi; Yuan, Junqing; Zhou, Wang
• 署名单位: Hong Kong University of Science & Technology; National University of Singapore
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08260
• 发表日期: 2009
• 页码: 1224-1232
• 关键词: confidence-intervals; U-statistics; BIAS

摘要

Empirical likelihood has been found very useful in many different occasions. However, when applied directly to some more complicated statistics such as U-statistics, it runs into serious computational difficulties. In this paper, we introduce a so-called jackknife empirical likelihood (JEL) method. The new method is extremely simple to use in practice. In particular. the JEL is shown to be very effective in handling one and two-sample U-statistics. The JEL can be potentially useful for other nonlinear statistics.