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A COMPARISON BETWEEN CLASSICAL ESTIMATORS AND BAYESIAN ESTIMATORS TO ESTIMATE THE RELIABILITY FUNCTION OF THE PARETO DISTRIBUTION TYPE-I
Keywords:
Pareto Type-I distribution, Bayesian estimators, classical estimators, loss function.DOI:
https://doi.org/10.17654/0972361722018Abstract
The estimation of the reliability function is important to find out the possibility of the machine and the system to work without failure for a long time, and this leads to reduce the expected time for faults in order to maintain the continuity of the production machines work, decrease the possibilities of sudden failure, increase the production capacity of machines and equipment. In this paper, we focused on the shape parameter estimation, reliability and the hazard function estimations of the Pareto Type-I distribution. Both classical and Bayesian approaches are considered with two different loss functions as squared error loss function (SELF) and linear exponential loss function (LLF). For the Bayesian method, both informative and non-informative priors are applied for the shape parameter. In addition, a comparison is made for the performance of the Bayes estimator and the maximum likelihood, via Monte Carlo simulation study based upon the mean square error and average estimate.
Received: December 1, 2021
Accepted: January 24, 2022
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