On Modeling Cancer and Tuberculosis Data Using the Birnbaum–Saunders Lifetime Model Established on a Logistic Kernel

Abstract

Due to their importance in representing, explaining, and analyzing phenomena, statistical lifetime distributions are widely used in science. As a result, this paper discusses a modern lifetime model called Birnbaum–Saunders logistic distribution. This distribution extends the Birnbaum–Saunders distribution, as it has proven to be characterized by great flexibility in data modeling in practice. Different features of this distribution have been discussed. The parameters of the model are estimated using the maximum likelihood and modified moment estimation methods. To evaluate the performance of the methods, a simulation study with data contamination scenarios is presented. Finally, the new model’s flexibility is tested using real datasets.