Discrete Bilal distribution with right-censored data

TL;DR

This paper develops inference methods for the discrete Bilal distribution with right-censored data, including maximum likelihood and Bayesian approaches, and demonstrates its effectiveness with real datasets.

Contribution

Introduces parameter estimation techniques for the discrete Bilal distribution with censored data and compares its performance to other discrete models.

Findings

The DB distribution performs comparably to traditional models.

Maximum likelihood and Bayesian methods are effective for censored data.

The model's flexibility is enhanced by including a cure fraction.

Abstract

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.