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Monotone composite quantile regression neural network for censored data with a cure fraction

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  • Zhang, Xinran
  • Yuan, Xiaohui
  • Wang, Chunjie
  • Song, Xinyuan

Abstract

The cure rate monotone composite quantile regression neural network model is investigated as an extension of the cure rate quantile model. It can uncover complex nonlinear relationships and effectively ensure the non-crossing of quantile predictions. An iterative algorithm coupled with data augmentation is developed to predict the survival time of susceptible subjects and the cure rate among all subjects. Simulation studies indicate that the proposed approach exhibits advantages in prediction over traditional statistical methods in finite samples when nonlinearity exists between response and predictors. The analysis of two real datasets further validates the utility of the proposed method.

Suggested Citation

  • Zhang, Xinran & Yuan, Xiaohui & Wang, Chunjie & Song, Xinyuan, 2025. "Monotone composite quantile regression neural network for censored data with a cure fraction," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000775
    DOI: 10.1016/j.csda.2025.108201
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Wenbin Lu, 2004. "On semiparametric transformation cure models," Biometrika, Biometrika Trust, vol. 91(2), pages 331-343, June.
    3. Zhang, Jiajia & Peng, Yingwei & Li, Haifen, 2013. "A new semiparametric estimation method for accelerated hazards mixture cure model," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 95-102.
    4. Mao, Meng & Wang, Jane-Ling, 2010. "Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 302-311.
    5. Jia, Yichen & Jeong, Jong-Hyeon, 2022. "Deep learning for quantile regression under right censoring: DeepQuantreg," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    6. Patilea, Valentin & Van Keilegom, Ingrid, 2020. "A general approach for cure models in survival analysis," LIDAM Reprints ISBA 2020042, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Yuanshan Wu & Guosheng Yin, 2017. "Multiple imputation for cure rate quantile regression with censored data," Biometrics, The International Biometric Society, vol. 73(1), pages 94-103, March.
    8. Yuanshan Wu & Guosheng Yin, 2013. "Cure Rate Quantile Regression for Censored Data With a Survival Fraction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1517-1531, December.
    9. López-Cheda, Ana & Cao, Ricardo & Jácome, M. Amalia & Van Keilegom, Ingrid, 2017. "Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 144-165.
    10. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
    11. Liu, Xiaoyu & Xiang, Liming, 2021. "Generalized accelerated hazards mixture cure models with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    12. Lopez-Cheda, Ana & Cao, Ricardo & Jacome, Amalia & Van Keilegom, Ingrid, 2017. "Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models," LIDAM Reprints ISBA 2017001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
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