Support Vector Regression for Right Censored Data

TL;DR

This paper introduces a unified support vector machine framework for right censored data, providing theoretical guarantees and demonstrating effectiveness through simulations.

Contribution

It develops a novel SVM approach for censored data with theoretical error bounds and applies it to various statistical estimations and classification tasks.

Findings

Finite sample bounds on generalization error

Risk consistency for broad probability measures

Effective performance demonstrated in simulations

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.