A generalized quantile regression model

被引：5

作者：

Nassiri, Vahid ^{[1
]}

Loris, Ignace ^{[2
]}

机构：

[1] Vrije Univ Brussel, Dept Math, Brussels, Belgium

[2] Univ Libre Bruxelles, Dept Math, Brussels, Belgium

来源：

JOURNAL OF APPLIED STATISTICS | 2013年 / 40卷 / 05期

关键词：

quantile regression; log-concave density; penalization; soft thresholding; outlier; long tail; THRESHOLDING ALGORITHM; SHRINKAGE; PROBABILITY; STATISTICS; HISTORY;

D O I：

10.1080/02664763.2013.780158

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：

A new class of probability distributions, the so-called connected double truncated gamma distribution, is introduced. We show that using this class as the error distribution of a linear model leads to a generalized quantile regression model that combines desirable properties of both least-squares and quantile regression methods: robustness to outliers and differentiable loss function.

引用

页码：1090 / 1105

页数：16

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