Estimation of the Generalized Weibull Distribution Parameters based on the Kernel and Bayes Methods with Real Data Applications
Abstract
M. Maswadah
In this work, a new estimation method using the kernel iteration technique based on the kernel estimation function has been stud- ied. This estimation method is presented as a new tool for estimation in statistical inference that has been applied for estimating the generalized Weibull distribution parameters. The generalized Weibull model parameter estimations were derived using the kernel and Bayes methods based on the generalized progressive hybrid censoring scheme via a Monte Carlo simulation. The simulation results indicated that the kernel estimation method is highly efficient and outperforms the Bayes estimation method based on the informative gamma and kernel priors using two different loss function Finally, two real data sets were studied to ensure the kernel estimation method can be used more effectively than the most popular estimation methods in fitting and ana- lyzing real lifetime data.