Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235867 
Year of Publication: 
2021
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2021-013
Publisher: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Abstract: 
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.
Subjects: 
Competing risks
Cumulative incidence function
Kaplan-Meier estimator
Redistribution method
JEL: 
C00
Document Type: 
Working Paper

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