Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 107, Issue 497, Pages 331-340Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2011.637468
Keywords
Censored data; Ensemble; Imputation; Random forests, Survival analysis; Trees
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Funding
- NIH [CA142538]
- NSF [DMS-0904184]
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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.
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