Support Vector Regression for Right Censored Data

2012/02/23 by Yair Goldberg, Goldberg, Yair, Michael R. Kosorok +1 · 1 citation
Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST) #math.ST #stat.ML #stat.TH

paper · pdf · doi:10.48550/arxiv.1202.5130

In this version, we strengthened the theoretical results and corrected a few mistakes

arxiv created 2013/01/12 · arxiv updated 2013/01/15

Abstract

We develop a unified approach for classification and regression support vector machines for data subject to right censoring. We provide finite sample bounds on the generalization error of the algorithm, prove risk consistency for a wide class of probability measures, and study the associated learning rates. We apply the general methodology to estimation of the (truncated) mean, median, quantiles, and for classification problems. We present a simulation study that demonstrates the performance of the proposed approach.

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