@article{Caparroz Lopes de Freitas_Achcar_Vinicius de Oliveira Peres_Martinez_2022, title={Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction}, volume={10}, url={http://iapress.org/index.php/soic/article/view/1414}, DOI={10.19139/soic-2310-5070-1414}, abstractNote={<p>The statistical literature presents many continuous probability distributions with only one parameter, which are extensively used in the analysis of lifetime data, such as the exponential, the Lindley, and the Rayleigh distributions. Alternatively, the use of discretized versions of these distributions can provide a better fit for the data in many applications. As the novelty of this study, we present inferences for the discrete Bilal distribution (DB) with one parameter introduced by Altun et al. (2020) in the presence of right-censored data and cure fraction. We assume standard maximum likelihood methods based on asymptotic normality of the maximum likelihood estimators and also a Bayesian approach based on MCMC (Markov Chain Monte Carlo) simulation methods to get inferences for the parameters of the discrete BD distribution. The use of the proposed model was illustrated with three examples considering real medical lifetime data sets. From these applications, we concluded that the proposed model based on the discrete DB distribution has good performance even with the inclusion of a cure fraction in comparison to other existing discrete models, such as the DsFx-I, Lindley, Rayleigh, and Burr-Hatke probability distributions. Moreover, the model can be easily implemented in standard existing software, such as the R package. Under a Bayesian approach, we assumed a gamma prior distribution for the parameter of the DB discrete distribution. We also provided a brief sensitivity analysis assuming the half-normal distribution in place of the gamma distribution for the parameter of the DB distribution. From the obtained results of this study, we can conclude that the proposed methodology can be very useful for researchers dealing with medical discrete lifetime data in the presence of right-censored data and cure fraction.</p>}, number={4}, journal={Statistics, Optimization & Information Computing}, author={Caparroz Lopes de Freitas, Bruno and Achcar, Jorge Alberto and Vinicius de Oliveira Peres, Marcos and Martinez, Edson Zangiacomi}, year={2022}, month={Aug.}, pages={1168-1186} }