Bayes Estimation of Parameters of the Kibble-Bivariate Gamma Distribution Under A Precautionary Loss Function for Fuzzy Data Using Simulation
Keywords:
Bayes estimation, Kibble-Bivariate, Fuzzy, Crisp set, Bayesian MethodsAbstract
In many real life applications, more than one variable needs to be studied. This means the need to model multivariate distributions to clarify the behavior of these variables combined and that there may be dependence among these variables. The parameters estimated according to the Bayesian method under the precautionary loss function are as close as possible to the real (hypothetical) parameters. The Bayes method under the squared loss function recorded a superiority over the Bayes method under the precautionary loss function at the cut-off coefficient (Alfa-cut = 0.3) in some simulation experiments. The greater the cutoff in the fuzzy group, the less elements that have less or equal cutoffs, and thus increase the accuracy of the estimation method.
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