Bayesian Approach for estimating the unknown Scale parameter of Erlang Distribution Based on General Entropy Loss Function
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Abstract
We are used Bayes estimators for unknown scale parameter when shape Parameter is known of Erlang distribution. Assuming different informative priors for unknown scale parameter. We derived The posterior density with posterior mean and posterior variance using different informative priors for unknown scale parameter which are the inverse exponential distribution, the inverse chi-square distribution, the inverse Gamma distribution, and the standard Levy distribution as prior. And we derived Bayes estimators based on the general entropy loss function (GELF) is used the Simulation method to obtain the results. we generated different cases for the parameters of the Erlang model, for different sample sizes. The estimates have been compared in terms of their mean-squared error (MSE). We concluded that the best estimators of the scale parameterof the Erlang distribution, based on GELF for the shape parameter (c=1,2,3) under inverse gamma prior with for all samples sizes(n) where the true cases of the Erlang model are and according to the smallest values of MSE
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