Document Detail


Consistency of Random Survival Forests.
MedLine Citation:
PMID:  20582150     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Hemant Ishwaran; Udaya B Kogalur
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Statistics & probability letters     Volume:  80     ISSN:  0167-7152     ISO Abbreviation:  -     Publication Date:  2010 Jul 
Date Detail:
Created Date:  2010-6-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101317307     Medline TA:  Stat Probab Lett     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1056-1064     Citation Subset:  -    
Affiliation:
Cleveland Clinic.
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
UL1 RR024989-01//NCRR NIH HHS; UL1 RR024989-02//NCRR NIH HHS

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