DOI QR코드

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The Doubly Regularized Quantile Regression

  • Choi, Ho-Sik (Department of Informational Statistics and Institute of Basic Science, Hoseo University) ;
  • Kim, Yong-Dai (Department of Statistics, Seoul National University)
  • 발행 : 2008.09.30

초록

The $L_1$ regularized estimator in quantile problems conduct parameter estimation and model selection simultaneously and have been shown to enjoy nice performance. However, $L_1$ regularized estimator has a drawback: when there are several highly correlated variables, it tends to pick only a few of them. To make up for it, the proposed method adopts doubly regularized framework with the mixture of $L_1$ and $L_2$ norms. As a result, the proposed method can select significant variables and encourage the highly correlated variables to be selected together. One of the most appealing features of the new algorithm is to construct the entire solution path of doubly regularized quantile estimator. From simulations and real data analysis, we investigate its performance.

키워드

참고문헌

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