Goodness-of-fit test for the Birnbaum-Saunders distribution based on the Kullback-Leibler information

In this work, we propose a goodness-of-fit test based on the Kullback-Leibler information for the Birnbaum-Saunders distribution. We use Monte Carlo simulations to evaluate the size and power of the proposed test for several alternative hypotheses under different sample sizes. We compare the powers with standard goodness-of-fit tests based as the Anderson-Darling and Cramer-von Mises tests. Finally, we illustrate the proposed test with a real data set to show its potential applications.