Solution path for quantile regression with epsilon-insensitive loss in a reproducing kernel Hilbert space

被引：0

作者：

Park, Jinho ^{[1
]}

机构：

[1] Inha Univ, Dept Stat, Incheon 22212, South Korea

来源：

STATISTICS & PROBABILITY LETTERS | 2017年 / 126卷

关键词：

Solution path; Quantile regression; Reproducing kernel Hilbert space;

D O I：

10.1016/j.spl.2017.03.006

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article proposes an algorithm for finding the solution paths of estimated quantile functions as the regularization parameter varies. The solution paths constitute a useful tool for choosing the optimal regularization parameter. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：205 / 211

页数：7

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