Title: Nonparametric Kernel Density Estimation for Univariate Curent Status Data,
Abstract: We derive estimators of the density of the event times of current status
data. The estimators are derived for the situations where the distribution of
the observation times is known and where this distribution is unknown. The
density estimators are constructed from kernel estimators of the density of
transformed current status data, which have a distribution similar to uniform
deconvolution data. Expansions of the expectation and variance as well as
asymptotic normality are derived. A reference density based bandwidth selection
method is proposed. A simulated example is presented.