International Journal of Applied Mathematics, Computational Science and Systems Engineering
E-ISSN: 2766-9823
Volume 5, 2023
Numerical Estimation Method for the Generalized Weibull Distribution Parameters
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Abstract: In this study, a new estimation method using the Runge-Kutta iteration technique is presented to improve point estimation methods. The improved method has been applied to the generalized Weibull distribution, which is a member of a family of distributions (T-X family). The estimates of the generalized Weibull model parameters were derived using the Runge-Kutta and Bayesian estimation methods based on the generalized progressive hybrid censoring scheme, via a Monte Carlo simulation. The simulation results indicated that the Runge-Kutta estimation method is highly efficient and outperforms the Bayesian estimation method based on the informative and kernel priors. Finally, two real data sets were studied to ensure the Runge-Kutta estimation method can be used more effectively than the most popular estimation methods in fitting and analyzing real lifetime data.
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Keywords: Bayesian inference, Generalized progressive hybrid censoring scheme, Informative prior, Kernel prior, Rung-Kutta method, Weibull model
Pages: 1-17
DOI: 10.37394/232026.2023.5.1