Support Vector Regression for Right Censored Data

Details Support Vector Regression for Right Censored Data Support Vector Regression for Right Censored Data

2012-02-23

arxiv.org/abs/1202.5130v1

Statistics

Machine Learning

Scientific paper

We develop a unified approach for support vector machines for classification and regression in which the outcomes are a function of the survival times subject to right censoring. We present a novel support-vector regression algorithm that is adjusted for censored data. We provide finite sample bounds on the generalization error of the algorithm. We prove risk consistency for a wide class of probability measures and study 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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