Tail index estimation, pareto quantile plots, and regression diagnostics

• 成果类型: Article
• 署名作者: Beirlant, J; Vynckier, P; Teugels, JL
• 署名单位: Fonds de la Recherche Scientifique - FNRS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291593
• 发表日期: 1996
• 页码: 1659-1667
• 关键词: Extreme value theory; Regular Variation; parameters; exponent; ORDER

摘要

Successful application of extreme value statistics for estimating the Pareto tail index relies heavily on the choice of the number of extreme values taken into account. It is shown that these tail index estimators can be considered estimates of the slope at the right upper tail of a Pareto quantile plot, obtained using a weighted least squares algorithm. From this viewpoint, based on classical ideas on regression diagnostics, algorithms can be constructed searching for that order statistic to the right of which one obtains an optimal linear fit of the quantile plot.