Confidence regions for high quantiles of a heavy tailed distribution

• 成果类型: Article
• 署名作者: Peng, Liang; Qi, Yongcheng
• 署名单位: University System of Georgia; Georgia Institute of Technology; University of Minnesota System; University of Minnesota Duluth
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000416
• 发表日期: 2006
• 页码: 1964-1986
• 关键词: empirical-likelihood; parameters; estimators; 2ND-ORDER; index

摘要

Estimating high quantiles plays an important role in the context of risk management. This involves extrapolation of an unknown distribution function. In this paper we propose three methods, namely, the normal approximation method, the likelihood ratio method and the data tilting method, to construct confidence regions for high quantiles of a heavy tailed distribution. A simulation study prefers the data tilting method.