Electronic Journal of Statistics

Support vector regression for right censored data

Yair Goldberg and Michael R. Kosorok

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We develop a unified approach for classification and regression support vector machines for when the responses are 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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Electron. J. Statist., Volume 11, Number 1 (2017), 532-569.

Received: February 2016
First available in Project Euclid: 2 March 2017

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Support vector regression right censored data generalization error universal consistency misspecification models

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Goldberg, Yair; Kosorok, Michael R. Support vector regression for right censored data. Electron. J. Statist. 11 (2017), no. 1, 532--569. doi:10.1214/17-EJS1231. https://projecteuclid.org/euclid.ejs/1488423807

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