Boundary-free estimators of the mean residual life function for data on general interval

Abstract

We propose two new kernel-type estimators of the mean residual life function m X of bounded or half-bounded interval-supported distributions. Though not as severe as the boundary problems in kernel density estimation, eliminating the boundary bias problems that occur in the naive kernel estimator of the mean residual life function is needed. In this article, we utilize the property of bijective transformation. Furthermore, our proposed methods preserve the mean value property, which cannot be done by the naive kernel estimator. Some simulation results showing the estimators’ performances and real data analysis will be presented in the last part of this article.