Abstract

In this paper, we discuss the class of generalized Birnbaum-Saunders distributions, which is a very flexible family suitable for modeling lifetime data as it allows for different degrees of kurtosis and asymmetry and unimodality as well as bimodality. We describe the theoretical developments on this model including properties, transformations and related distributions, lifetime analysis, and shape analysis. We also discuss methods of inference based on uncensored and censored data, diagnostics methods, goodness-of-fit tests, and random number generation algorithms for the generalized Birnbaum-Saunders model. Finally, we present some illustrative examples and show that this distribution fits the data better than the classical Birnbaum-Saunders model.