Weighted Competing Risks Quantile Regression Models and Variable Selection
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 se...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1295 |
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| author | Erqian Li Jianxin Pan Manlai Tang Keming Yu Wolfgang Karl Härdle Xiaowen Dai Maozai Tian |
| author_facet | Erqian Li Jianxin Pan Manlai Tang Keming Yu Wolfgang Karl Härdle Xiaowen Dai Maozai Tian |
| author_sort | Erqian Li |
| collection | DOAJ |
| description | 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. Real data about bone marrow transplant (BMT) are also analyzed to illustrate the application of the proposed procedure. |
| first_indexed | 2024-03-11T06:13:35Z |
| format | Article |
| id | doaj.art-a81d7a747fc84d4d829e276565a0a612 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:35Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a81d7a747fc84d4d829e276565a0a6122023-11-17T12:26:36ZengMDPI AGMathematics2227-73902023-03-01116129510.3390/math11061295Weighted Competing Risks Quantile Regression Models and Variable SelectionErqian Li0Jianxin Pan1Manlai Tang2Keming Yu3Wolfgang Karl Härdle4Xiaowen Dai5Maozai Tian6College of Science, North China University of Technology, Beijing 100144, ChinaSchool of Mathematics, University of Manchester, Manchester M13 9PL, UKDepartment of Physics, Astronomy and Mathematics, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL10 9EU, UKDepartment of Mathematics, College of Engineering, Design and Physical Sciences Brunel University, Uxbridge UB8 3PH, UKSchool of Business and Economics, Humboldt-Universität zu Berlin, 10117 Berlin, GermanySchool of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201620, ChinaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaThe 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. Real data about bone marrow transplant (BMT) are also analyzed to illustrate the application of the proposed procedure.https://www.mdpi.com/2227-7390/11/6/1295competing riskscumulative incidence functionbone marrow transplantre-distribution method |
| spellingShingle | Erqian Li Jianxin Pan Manlai Tang Keming Yu Wolfgang Karl Härdle Xiaowen Dai Maozai Tian Weighted Competing Risks Quantile Regression Models and Variable Selection Mathematics competing risks cumulative incidence function bone marrow transplant re-distribution method |
| title | Weighted Competing Risks Quantile Regression Models and Variable Selection |
| title_full | Weighted Competing Risks Quantile Regression Models and Variable Selection |
| title_fullStr | Weighted Competing Risks Quantile Regression Models and Variable Selection |
| title_full_unstemmed | Weighted Competing Risks Quantile Regression Models and Variable Selection |
| title_short | Weighted Competing Risks Quantile Regression Models and Variable Selection |
| title_sort | weighted competing risks quantile regression models and variable selection |
| topic | competing risks cumulative incidence function bone marrow transplant re-distribution method |
| url | https://www.mdpi.com/2227-7390/11/6/1295 |
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