On the Least Squares Cross-Validation Bandwidth in Hazard Rate Estimation

Abstract

It is known that the least squares cross-validation bandwidth is asymptotically optimal in the case of kernel-based density and hazard rate estimation in the settings of both complete and randomly right-censored samples. From a practical point of view, it is important to know at what rate the cross-validation bandwidth converges to the optimal. In this paper we answer this question in a general setup which unifies all four possible cases.