Bayes bandwidth selection in kernel density estimation with censored data

Abstract

Problems with censored data arise frequently in survival analyses and reliability applications. The estimation of the density function of the failure times is often of interest. Two inherent problems in density estimation for lifetime data are the spill-over at the origin and the smoothing parameter selection. To address these issues, we propose the use of asymmetric kernels (like inverse Gaussian) with bandwidths selected by a Bayes criterion. We show strong pointwise consistency of the density estimator, and for suitable choices of the prior, we show that one can obtain meaningful bandwidths with the same rates of convergence as for the classical asymptotically optimal bandwidths.