  We propose two new kernel-type estimators of the mean residual life function
$m_X(t)$ of bounded or half-bounded interval supported distributions. Though
not as severe as the boundary problems in the 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 a
real data analysis will be presented in the last part of this article.
