Support Vector Machines for Current Status Data
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Current status data is a data format where the time to event is restricted to knowledge of whether or not the failure time exceeds a random monitoring time. We develop a support vector machine learning method for current status data that estimates the failure time expectation as a function of the covariates. In order to obtain the support vector machine decision function, we minimize a regularized version of the empirical risk with respect to a data-dependent loss. We show that the decision function has a closed form. Using finite sample bounds and novel oracle inequalities, we prove that the obtained decision function converges to the true conditional expectation for a large family of probability measures and study the associated learning rates. Finally we present a simulation study that compares the performance of the proposed approach to current state of the art.
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