title: Recursively Imputed Survival Trees.
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 to previous methods.
authors.affiliation: Ruoqing Zhu is a doctoral student, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 ( rzhu@live.unc.edu ). Michael R. Kosorok is Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 ( kosorok@unc.edu ).
authors.forename: Ruoqing
authors.initials: R
authors.lastname: Zhu
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok