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Li, Erqian [VerfasserIn]; Pan, Jianxin [VerfasserIn]; Tang, Manlai [VerfasserIn]; Yu, Keming [VerfasserIn]; Härdle, Wolfgang Karl [VerfasserIn]; Dai, Xiaowen [VerfasserIn]; Tian, Maozai [VerfasserIn] ; Zhan, Yiqiang [MitwirkendeR]; Liu, Xingrong [MitwirkendeR]

Weighted Competing Risks Quantile Regression Models and Variable Selection

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  • Medientyp: E-Artikel
  • Titel: Weighted Competing Risks Quantile Regression Models and Variable Selection
  • Beteiligte: Li, Erqian [VerfasserIn]; Pan, Jianxin [VerfasserIn]; Tang, Manlai [VerfasserIn]; Yu, Keming [VerfasserIn]; Härdle, Wolfgang Karl [VerfasserIn]; Dai, Xiaowen [VerfasserIn]; Tian, Maozai [VerfasserIn]
  • Erschienen: Humboldt-Universität zu Berlin, 2023-03-08
  • Sprache: Englisch
  • DOI: https://doi.org/10.3390/math11061295; https://doi.org/10.18452/26463
  • ISSN: 2227-7390
  • Schlagwörter: competing risks ; re-distribution method ; cumulative incidence function ; bone marrow transplant
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: 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. ; National Natural Science Foundation of China ; Scientific Research Foundation of North China University of Technology ; Fundamental Research Funds for Beijing Universities, NCUT ; National Natural Science Foundation of China ; China Statistical Research Project ; Peer Reviewed
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung (CC BY)