Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Quantile Regression with Non-Convex Penalty on High-Dimensions

  • Choi, Ho-Sik (Dept. of Informational Statistics and Institute of Basic Science, Hoseo Univ.) ;
  • Kim, Yong-Dai (Dept. of Statistics, Seoul National Univ.) ;
  • Han, Sang-Tae (Dept. of Informational Statistics, Hoseo Univ.) ;
  • Kang, Hyun-Cheol (Dept. of Informational Statistics, Hoseo Univ.)
  • Published : 2009.01.31
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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 $L_1$ norm.

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References

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