Using Generalized Square Loss Function to Estimate the Shape Parameter of the Burr Type XII Distribution

In this paper we compare the performance of Bayes estimators for the shape parameter of the Burr Type XII distribution with the classical minimum mean square error (MinMSE) estimator. We considered the generalized square loss function (GSLF) with Jeffreys prior, as well as the exponential prior. The comparison was made through a Monte Carlo simulation study with respect to the mean square error MSE. The results of comparison show that Bayes estimators of the shape parameter under the GSLF with the exponential prior gives better results among other estimators. Accordingly; if adequate information is available about the parameters it is preferable to use conjugate informative priors