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

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 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.

KEYWORDS:

• Censored data
• composite quantile regression
• inverse-censoring-probability
• quantile regression
• SCAD

MATHEMATICS SUBJECT CLASSIFICATION:

• Statistics
• Survival analysis and Censored data

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 11171361, 11671059) and Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20110191110033).