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AUTHOR(S): M. Maswadah
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ABSTRACT In parameter estimation techniques, there are many methods for estimating the distribution parameters in life data analysis. However, most of them are less efficient than the Bayes’ method based on the informative prior. Thus, the main objective of this study is to introduce an optimal integrable estimation method for estimating the Burr type-XII distribution parameters and compare them with the Bayesian estimates based on the informative gamma and kernel priors. A comparison between these estimators is provided by using an extensive Monte Carlo simulation based on two criteria, namely, the absolute value bias and mean squared error. The simulation results indicated that the new integrable method is highly favorable, which provides better estimates and outperforms the Bayes’ method using different loss functions based on the generalized progressive hybrid censoring scheme. Finally, two real datasets analyses are presented to illustrate the efficiency of the proposed methods. |
KEYWORDS Bayes estimation; Compound LINEX loss function; Informative prior; Integrable estimation method; Kernel prior |
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Cite this paper M. Maswadah. (2025) Estimation of the Three-Parameter Burr-XII Model Parameters based on the Integrable Estimation Method. International Journal of Mathematical and Computational Methods, 10, 283-297 |
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