Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 013

Penalized Weigted Competing Risks Models Based on Quantile Regression

Erqian Li
Wolfgang Karl Härdle
Xiaowen Dai
Maozai Tian

The proportional subdistribution hazards (PSH) model is popularly used to deal
with competing risks data. Censored quantile regression provides an important
supplement as well as variable selection methods, due to large numbers of
irrelevant covariates in practice. In this paper, we study variable selection
procedures based on penalized weighted quantile regression for competing risks
models, which is conveniently applied by researchers. Asymptotic properties of
the proposed estimators including consistency and asymptotic normality of non-
penalized estimator and consistency of variable selection are established. Monte
Carlo simulation studies are conducted, showing that the proposed methods are
considerably stable and efficient. A real data about bone marrow transplant
(BMT) is also analyzed to illustrate the application of proposed procedure.

Competing risks, Cumulative incidence function, Kaplan-Meier estimator,
Redistribution method

JEL Classification: