Conditional Inference on the Burr Type-XII Distribution Parameters

Abstract It is widely known that conditional inference is usually just as effective as Bayesian inference based on non-informative priors. However, it is less efficient than Bayesian inference based on informative priors. Therefore, the main objective of this work is to apply Bayes’ theorem to the conditional distribution of the pivotal functions for finding the estimates of Burr type-XII model parameters based on the kernel prior of the pivotal functions and compare them with Bayes estimates, via Monte Carlo simulations. The simulation results showed that conditional inference is highly efficient and provides better estimates than Bayes’ method based on different loss functions. Finally, two real data sets were applied to demonstrate the efficiency of the proposed methods