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Recursively Imputed Survival Trees

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  • Ruoqing Zhu
  • Michael R. Kosorok

Abstract

We propose recursively imputed survival tree (RIST) regression for right-censored data. This new nonparametric regression procedure uses a novel recursive imputation approach combined with extremely randomized trees that allows significantly better use of censored data than previous tree-based methods, yielding improved model fit and reduced prediction error. The proposed method can also be viewed as a type of Monte Carlo EM algorithm, which generates extra diversity in the tree-based fitting process. Simulation studies and data analyses demonstrate the superior performance of RIST compared with previous methods.

Suggested Citation

  • Ruoqing Zhu & Michael R. Kosorok, 2012. "Recursively Imputed Survival Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 331-340, March.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:497:p:331-340
    DOI: 10.1080/01621459.2011.637468
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    Cited by:

    1. Ruoqing Zhu & Donglin Zeng & Michael R. Kosorok, 2015. "Reinforcement Learning Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1770-1784, December.
    2. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    3. Yingchao Zhong & Chang Wang & Lu Wang, 2021. "Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare," Stats, MDPI, vol. 4(4), pages 1-17, September.
    4. Yifei Sun & Sy Han Chiou & Mei‐Cheng Wang, 2020. "ROC‐guided survival trees and ensembles," Biometrics, The International Biometric Society, vol. 76(4), pages 1177-1189, December.
    5. Alexander Hanbo Li & Jelena Bradic, 2019. "Censored Quantile Regression Forests," Papers 1902.03327, arXiv.org.

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