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Weighted composite quantile regression with censoring indicators missing at random

Author

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  • Jiang-Feng Wang
  • Wei-Jun Jiang
  • Fang-Yin Xu
  • Wu-Xin Fu

Abstract

In this paper, we consider the weighted composite quantile regression for the linear model when the data are right censored and the censoring indicators are missing at random. The adaptive penalized procedures are proposed to discuss variable selection in the model. Under appropriate assumptions, the asymptotic normality and oracle property of these estimators are also established. The simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

Suggested Citation

  • Jiang-Feng Wang & Wei-Jun Jiang & Fang-Yin Xu & Wu-Xin Fu, 2021. "Weighted composite quantile regression with censoring indicators missing at random," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(12), pages 2900-2917, June.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:12:p:2900-2917
    DOI: 10.1080/03610926.2019.1678638
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    Cited by:

    1. Zou, Yuye & Wu, Chengxin, 2023. "Composite quantile regression analysis of survival data with missing cause-of-failure information and its application to breast cancer clinical trial," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

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