Recursively imputed survival trees

Ruoqing Zhu, Michael R. Kosorok

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)331-340
Number of pages10
JournalJournal of the American Statistical Association
Volume107
Issue number497
DOIs
StatePublished - 2012
Externally publishedYes

Keywords

  • Censored data
  • Ensemble
  • Imputation
  • Random forests
  • Survival analysis
  • Trees

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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