Support Vector Regression for Right Censored Data
This addresses the problem of analyzing incomplete survival data for researchers in statistics and machine learning, representing an incremental extension of support vector methods to censored data.
The authors developed a unified support vector machine approach for classification and regression with right-censored data, providing finite sample error bounds, proving risk consistency, and demonstrating performance through simulations.
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.