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
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
收藏
页码:1090 / 1105
页数:16
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