Bayes Estimator for Dagum Distribution Parameters Using Non-Informative Prior Rules with K-Loss Function and Entropy Loss Function

Abstract

The parameter estimator discussed is the p parameter estimator of the Dagum distribution with the K-loss function and the entropy loss function using the Bayes method. To get the Bayes estimator from the scale parameter of the Dagum distribution, the Jeffrey non-informative prior distribution is used based on the maximum likelihood function and the loss function for the K-loss function and the entropy loss function to obtain an efficient estimator. Determination of the best estimator is done by comparing the variance values generated from each estimator. An estimator that uses the entropy loss function is the best method for estimating the parameters of the Dagum distribution of the data population with efficient conditions met.