Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations
DOI:
https://doi.org/10.57805/revstat.v21i3.174Keywords:
Bayesian estimation, Burr type XII distribution, expectation-maximization algorithm, fuzzy observations, Lindley’s approximation, maximum likelihood estimation, Tierney-Kadane approximationAbstract
In this article, the classical as well as the Bayesian estimation problems of two-parameter Burr type XII distribution based on fuzzy data are considered. The maximum likelihood estimators via two methods, namely, Newton-Raphson and Expectation-Maximization algorithms are computed. The standard errors of the estimates are computed using the observed information matrix. For computing the Bayes estimators, three methods viz Lindley’s approximation, Tierney-Kadane approximation and highest posterior density method are obtained. Monte-Carlo simulation experiments are conducted to investigate the performance of the proposed methods. Finally, the proposed methods are illustrated by using three different real data sets.
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