This paper presents inferences for the discrete Bilal (DB) distribution introduced by Altun et al. (2020). We consider parameter estimation for DB distribution in the presence of randomly right-censored data.We use maximum likelihood and Bayesian methods for the estimation of the model parameters. We also consider the inclusion of a cure fraction in the model. The usefulness of the proposed model was illustrated with three examples considering real datasets. These applications suggested that the model based on DB distribution performs at least as good as some other traditional discrete models as the DsFx-I, discrete Lindley, discrete Rayleigh, and discrete Burr- Hatke distributions. R codes are provided in an appendix at the end of the paper so that reader can carry out their own analysis.

翻译：本文件介绍了Altun等人(2020年)采用的离散比拉勒(DB)分布的推论。我们考虑在有随机右审查数据的情况下对DB分布的参数估计。我们使用最大可能性和巴伊西亚方法来估计模型参数。我们还考虑在模型中包括一个解药分数。用三个例子来说明拟议模型的有用性,三个例子考虑了真实的数据集。这些应用表明,基于DB分布的模型至少与DsFx-I、离散Lindley、离散Rayleigh和离散Burr-Hatke分布的其他传统离散模型相同。在本文结尾的附录中提供了R代码,以便读者能够进行自己的分析。