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Weighted composite quantile estimation and variable selection method for censored regression model

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  • Tang, Linjun
  • Zhou, Zhangong
  • Wu, Changchun

Abstract

This paper considers the weighted composite quantile (WCQ) regression for linear model with random censoring. The adaptive penalized procedure for variable selection in this model is proposed, and the consistency, asymptotic normality and oracle property of the resulting estimators are also derived. The simulation studies and the analysis of an acute myocardial infarction data set are conducted to illustrate the finite sample performance of the proposed method.

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  • Tang, Linjun & Zhou, Zhangong & Wu, Changchun, 2012. "Weighted composite quantile estimation and variable selection method for censored regression model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 653-663.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:3:p:653-663
    DOI: 10.1016/j.spl.2011.11.021
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    Cited by:

    1. Lu Li & Ruiting Hao & Xiaorong Yang, 2024. "Data Augmentation Based Quantile Regression Estimation for Censored Partially Linear Additive Model," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1083-1112, August.
    2. Chengxin Wu & Nengxiang Ling & Philippe Vieu & Guoliang Fan, 2025. "Composite quantile estimation in partially functional linear regression model with randomly censored responses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(1), pages 28-47, March.
    3. Gabriela Ciuperca, 2025. "Right-censored models by the expectile method," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(1), pages 149-186, January.
    4. Jiang, Rong & Zhou, Zhan-Gong & Qian, Wei-Min & Chen, Yong, 2013. "Two step composite quantile regression for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 180-191.
    5. Xiaohui Yuan & Yong Li & Xiaogang Dong & Tianqing Liu, 2022. "Optimal subsampling for composite quantile regression in big data," Statistical Papers, Springer, vol. 63(5), pages 1649-1676, October.
    6. Qibing Gao & Xiuqing Zhou & Yanqin Feng & Xiuli Du & XiaoXiao Liu, 2021. "An empirical likelihood method for quantile regression models with censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 75-96, January.
    7. Tang, Linjun & Zhou, Zhangong & Wu, Changchun, 2013. "Testing the linear errors-in-variables model with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 875-884.
    8. Wang, Jiang-Feng & Ma, Wei-Min & Zhang, Hui-Zeng & Wen, Li-Min, 2013. "Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data," Statistics & Probability Letters, Elsevier, vol. 83(6), pages 1571-1579.
    9. Yuzhu Tian & Manlai Tang & Maozai Tian, 2018. "Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies," Computational Statistics, Springer, vol. 33(4), pages 1563-1587, December.
    10. Jiang, Rong & Qian, Wei-Min & Zhou, Zhan-Gong, 2016. "Weighted composite quantile regression for single-index models," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 34-48.
    11. Ning, Zijun & Tang, Linjun, 2014. "Estimation and test procedures for composite quantile regression with covariates missing at random," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 15-25.
    12. Hu Yang & Huilan Liu, 2016. "Penalized weighted composite quantile estimators with missing covariates," Statistical Papers, Springer, vol. 57(1), pages 69-88, March.

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