Quantile regression in reproducing kernel Hilbert spaces

被引：139
|
作者：
Li, Youjuan [1 ]
Liu, Yufeng
Zhu, Ji
机构：
[1] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA
[2] Univ N Carolina, Dept Stat & Operat Res, Carolina Ctr Genome Sci, Chapel Hill, NC 27599 USA
来源：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2007年 / 102卷 / 477期
基金：
美国国家科学基金会;
关键词：
degrees of freedom; metric entropy; model selection; quadratic programming; quantile regression; reproducing kernel; Hilbert space;
D O I：
10.1198/016214506000000979
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：
In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptotic theory for the KQR model.
引用
收藏
页码：255 / 268
页数：14
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