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Abstract Details
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Activity Number:
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256
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301430 |
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Title:
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Recursively Imputed Survival Trees
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Author(s):
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Ruoqing Zhu*+ and Michael R. Kosorok
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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3105 McGavran-Greenberg Hall, CB #7420 , Chapel Hill, NC, 27599-7420,
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Keywords:
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Trees ;
Ensemble ;
Survival Analysis ;
Variable Selection ;
Imputation
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Abstract:
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Tree-based methods have become increasingly popular statistical tools since Breiman et al. (1984) introduced the classification and regression tree (CART) to the statistics research community. We adept tree-based method into survival arena and yield a highly accurate nonparametric survival model for right censored survival data: Recursively Imputed Survival Tree (RIST) regression. A novel censoring imputation method is purposed to generate failure times for censored observations. This imputation method utilize censored observations in a way like no previous method and result in a better model fitting and prediction. We also discuss the consistency of tree-based method and its future application in variable selection.
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