Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data

In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile. However, the investigation of the tail quantile is somewhat difficult because of data sparsity. This paper challenges to develop the nonpar...

詳細記述

書誌詳細

出版年:	Revstat Statistical Journal
第一著者:	Takuma Yoshida
フォーマット:	論文
言語:	英語
出版事項:	Instituto Nacional de Estatística | Statistics Portugal 2021-07-01
主題:	asymptotic normality extrapolation extremal quantile regression extreme value theory nonparametric estimator
オンライン･アクセス:	https://revstat.ine.pt/index.php/REVSTAT/article/view/346