2021/11/02 by Bruno Caparroz Lopes de Freitas, Jorge Alberto Achcar, de Freitas, Bruno Caparroz Lopes +6
Environmental Science · Mathematics · #62F15 #62N01 #62N02 #Algorithm #Applications (stat.AP) #Applied mathematics #Bayesian probability #Computer science #Discrete time and continuous time #Distribution (mathematics) #FOS: Computer and information sciences #Hydrology and Drought Analysis #Mathematical analysis #Mathematics #Maximum likelihood #Methodology (stat.ME) #Probability density function #Rayleigh distribution #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #Statistics #Truncation (statistics) #msc:62F15 #msc:62N01 #msc:62N02 #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2111.01943
20 pages, 10 figures
arxiv created 2021/11/02 · openalex publication_date 2021/11/02 · arxiv updated 2021/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.