Consistency of random survival forests

Hemant Ishwaran, Udaya B. Kogalur

Research output: Contribution to journalArticle

76 Scopus citations

Abstract

We prove uniform consistency of Random Survival Forests (RSF), a newly introduced forest ensemble learner for analysis of right-censored survival data. Consistency is proven under general splitting rules, bootstrapping, and random selection of variables-that is, under true implementation of the methodology. Under this setting we show that the forest ensemble survival function converges uniformly to the true population survival function. To prove this result we make one key assumption regarding the feature space: we assume that all variables are factors. Doing so ensures that the feature space has finite cardinality and enables us to exploit counting process theory and the uniform consistency of the Kaplan-Meier survival function.

Original languageEnglish (US)
Pages (from-to)1056-1064
Number of pages9
JournalStatistics and Probability Letters
Volume80
Issue number13-14
DOIs
StatePublished - Jul 1 2010
Externally publishedYes

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Keywords

  • Consistency
  • Ensemble
  • Factors
  • Kaplan-Meier
  • Random forests

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

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