  In survival analysis, estimating the failure time distribution is an
important and difficult task, since usually the data is subject to censoring.
Specifically, in this paper we consider current status data, a type of data
where all of the observations are censored. The format of the data is such that
the failure time is restricted to knowledge of whether or not the failure time
exceeds a random monitoring time. We propose a flexible kernel machine approach
for estimation of the failure time expectation as a function of the covariates,
with current status data. In order to obtain the kernel machine decision
function, we minimize a regularized version of the empirical risk with respect
to a new loss function. Using finite sample bounds and novel oracle
inequalities, we prove that the obtained estimator converges to the true
conditional expectation for a large family of probability measures. Finally, we
present a simulation study and an analysis of real-world data that compares the
performance of the proposed approach to existing methods. We show empirically
that our approach is comparable to current state of the art, and in some cases
is even better.
