Arbeitspapier
Penalized weigted competing risks models based on quantile regression
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.
- Language
-
Englisch
- Bibliographic citation
-
Series: IRTG 1792 Discussion Paper ; No. 2021-013
- Classification
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Subject
-
Competing risks
Cumulative incidence function
Kaplan-Meier estimator
Redistribution method
- Event
-
Geistige Schöpfung
- (who)
-
Li, Erqian
Härdle, Wolfgang
Dai, Xiaowen
Tian, Maozai
- Event
-
Veröffentlichung
- (who)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (where)
-
Berlin
- (when)
-
2021
- Handle
- Last update
- 10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Li, Erqian
- Härdle, Wolfgang
- Dai, Xiaowen
- Tian, Maozai
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Time of origin
- 2021
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