Kernel Estimation of Entropy for the Weibull Distribution Using Generalized Progressive Hybrid Censored Data
Abstract
M. Maswadah
In recent years, numerical methods have been widely and effectively applied in estimating parameters of lifetime distributions. Therefore, the primary objective of this paper is to introduce a novel numerical estimation approach namely, the kernel estimation method for estimating both the entropy and the parameters of the Weibull distribution, and to compare its performance with the commonly used Bayes estimation method in statistical inference. Through an extensive Monte Carlo simulation study, the entropy and distribution parameters are estimated using both techniques. The simulation results demonstrate that the kernel method generally outperforms the Bayes method, particularly when using informative gamma and kernel priors. In addition, real data applications are presented to illustrate the proposed methods and to compare their practical performance.