A new log-linear bimodal Birnbaum-Saunders regression model with application to survival data

The log-linear Birnbaum-Saunders model has been widely used in empirical applications. We introduce an extension of this model based on a recently proposed version of the Birnbaum-Saunders distribution which is more flexible than the standard Birnbaum-Saunders law since its density may assume both unimodal and bimodal shapes. We show how to perform point estimation, interval estimation and hypothesis testing inferences on the parameters that index the regression model we propose. We also present a number of diagnostic tools, such as residual analysis, local influence, generalized leverage, generalized Cook's distance and model misspecification tests. We investigate the usefulness of model selection criteria and the accuracy of prediction intervals for the proposed model. Results of Monte Carlo simulations are presented. Finally, we also present and discuss an empirical application.