Bandwidth selection for estimating the mean residual life function

Mean Residual Life (MRL) function has been of considerable interest in a wide range offields. MRL has been applied to reliability theory, technical systems and survivorship studies in biomedicine, among many others. Non-parametric approach has been used to estimate MRLfunction and it is an appealing technique to use in this regard since it does not impose many restrictions on the underlying probability distribution. Local Linear (LL) estimator is a non-parametric kernel based technique recently developed which is based on local linear fitting techniques. LL estimator is known to reduce bias. and boundary effects that exist in kernel estimators. In this papel; we discuss the performance of this estimator for MRL lIsing different kernels and bandwidths. The investigation reveals that the pelfonnance of this estimator for distributions (or models) with a Decreasing Mean Residual Life (DMRL) fill1ction is excellent. In addition, the Mean Squared Error (MSE) of LL estimator is considerably low when compared to that of empirical version of MRLfunction. We also del1101istrate that pelfonnance varies significantly with different bandwidth selection methods