Penalized composite quantile estimation for censored regression model with a diverging number of parameters

Huilan Liu and Hu Yang

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 13, 6558-6578

Abstract: This article considers the variable selection in censored composite quantile regression with a diverging number of parameters. We propose a sparse weighted composite quantile regression objective function based on inverse censoring probability weighting and smoothly clipped absolute deviation penalty. Under some mild conditions, we get n/pn$\sqrt{n/p_{n}}$ consistency and “Oracle Property” of the proposed estimator. Moreover, we use an iterative algorithm to minimize the proposed objective function, and a modified Bayesian information criterion for tuning parameter selection. Some simulations and real data examples are provided to examine the performance of our procedure.

Date: 2017
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DOI: 10.1080/03610926.2015.1130840

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