Random survival forests

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We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.
Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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http://salesiana.dossiersoluciones.com/handle/123456789/173848

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0811.1645
 http://arxiv.org/abs/0811.1645
 Annals of Applied Statistics 2008, Vol. 2, No. 3, 841-860
 doi:10.1214/08-AOAS169

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arXiv