Quantile Regression with Non-Convex Penalty on High-Dimensions

Title & Authors
Quantile Regression with Non-Convex Penalty on High-Dimensions
Choi, Ho-Sik; Kim, Yong-Dai; Han, Sang-Tae; Kang, Hyun-Cheol;

Abstract
In regression problem, the SCAD estimator proposed by Fan and Li (2001), has many desirable property such as continuity, sparsity and unbiasedness. In this paper, we extend SCAD penalized regression framework to quantile regression and hence, we propose new SCAD penalized quantile estimator on high-dimensions and also present an efficient algorithm. From the simulation and real data set, the proposed estimator performs better than quantile regression estimator with $\small{L_1}$ norm.
Keywords
Language
English
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