Detecting influential data points for the Hill estimator in Pareto-type distributions

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• 作者：Mia Hubert^a ; ^{Mia.Hubert@wis.kuleuven.be} ; Goedele Dierckx^a ; ^b ; Dina Vanpaemel^a
• 关键词：Pareto-type distribution ; Extreme value index ; Tail index estimation ; Influential data points ; Robustness
• 刊名：Computational Statistics and Data Analysis
• 出版年：2013
• 出版时间：September, 2013
• 年：2013
• 卷：65
• 期：Complete
• 页码：13-28
• 全文大小：772 K

文摘

Pareto-type distributions are extreme value distributions for which the extreme value index . Classical estimators for , like the Hill estimator, tend to overestimate this parameter in the presence of outliers. The empirical influence function plot, which displays the influence that each data point has on the Hill estimator, is introduced. To avoid a masking effect, the empirical influence function is based on a new robust GLM estimator for . This robust GLM estimator is used to determine high quantiles of the data generating distribution, allowing to flag data points as unusually large if they exceed this high quantile.