Support vector regression for right censored data

Yair Goldberg; Michael R. Kosorok

doi:10.1214/17-EJS1231

Support vector regression for right censored data

Yair Goldberg, Michael R. Kosorok

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	532-569
Number of pages	38
Journal	Electronic Journal of Statistics
Volume	11
Issue number	1
DOIs	https://doi.org/10.1214/17-EJS1231
State	Published - 2017

Bibliographical note

Funding Information:
The authors thank Danyu Lin for many helpful discussions and suggestions. The first author was funded in part by a Gillings Innovation Laboratory (GIL) award at the UNC Gillings School of Global Public Health and by Norsk SykepleierforbundNSF grant DMS-1407732. The second author was funded in part by NCINational Cancer Institute grant CA142538 and by NSFNorsk Sykepleierforbund grant DMS-1407732.

Publisher Copyright:
© 2017, Institute of Mathematical Statistics. All rights reserved.

Keywords

Generalization error
Misspecification models
Right censored data
Support vector regression
Universal consistency

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1214/17-EJS1231

Cite this

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Support vector regression for right censored data

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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