Hazard rate function estimation using Erlang kernel
Raid B. Salha, Hazem I. El Shekh Ahmed, Iyad M. Alhoubi · Pure Mathematical Sciences · 2014
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, we define the Erlang kernel and use it to nonparametically estimation of the probability density function (pdf) and the hazard rate function for inde-pendent and identically distributed (iid) data.The bias, variance and the optimal bandwidth of the proposed estimator are investigated. Moreover, the asymptotic normality of the proposed estimator is investigated. The performance of the pro-posed estimator is tested using simulation study and real data.