Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction

In this paper, we introduce maximum likelihood and Bayesian parameter estimation for the exponentiated discrete Weibull (EDW) distribution in the presence of randomly right-censored data. We also consider the inclusion of a cure fraction in the model. The performance of the maximum likelihood estimation approach is assessed by conducting an extensive simulation study with different sample sizes and different values for the parameters of the EDW distribution. The uselfuness of the proposed model is illustrated with two examples considering real data sets.