Interquantile shrinkage and variable selection in quantile regression

被引:33
作者
Jiang, Liewen [1 ]
Bondell, Howard D. [1 ]
Wang, Huixia Judy [1 ]
机构
[1] N Carolina State Univ, Dept Stat, Raleigh, NC 27606 USA
基金
美国国家科学基金会;
关键词
Fused adaptive Lasso; Fused adaptive sup-norm; Oracle; Quantile regression; Smoothing; Variable selection; MODEL SELECTION; INFERENCE;
D O I
10.1016/j.csda.2013.08.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Examination of multiple conditional quantile functions provides a comprehensive view of the relationship between the response and covariates. In situations where quantile slope coefficients share some common features, estimation efficiency and model interpretability can be improved by utilizing such commonality across quantiles. Furthermore, elimination of irrelevant predictors will also aid in estimation and interpretation. These motivations lead to the development of two penalization methods, which can identify the interquantile commonality and nonzero quantile coefficients simultaneously. The developed methods are based on a fused penalty that encourages sparsity of both quantile coefficients and interquantile slope differences. The oracle properties of the proposed penalization methods are established. Through numerical investigations, it is demonstrated that the proposed methods lead to simpler model structure and higher estimation efficiency than the traditional quantile regression estimation. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:208 / 219
页数:12
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