A new proof of strong consistency of kernel estimation of density function and mode under random censorship

Abstract

In this paper, we establish a new proof of uniform consistency of kernel estimator of density function when we observe a random right censored model. This proof uses an exponential inequality established by Wang (2000). As a consequence, we obtain the almost sure convergence of the kernel estimator of the mode.Censored data Kaplan-Meier estimator Kernel density estimation Mode estimation Strong consistency