A new discrete Burr III distribution for modeling radiation and biological data

A new three-parameter discrete probability distribution entitled “discrete modified Burr III” distribution is introduced using the survival discretization approach. We derived and explored various statistical properties such as moments, dispersion index, mean deviation, and order statistics. Additionally, we also explore its reliability characteristics including survival, hazard function, cumulative hazard function, and residual reliability function. The parameters of the model are estimated using the maximum likelihood estimation approach. A detailed Monte Carlo simulation is utilized to assess the performance of derived estimators across varying sample sizes. The utility of the new model is evaluated using biological and radiation datasets, demonstrating its better fit compared to considered competing discrete distribution. Additionally, we also analyzed these datasets using the Bayesian estimation approach.

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Publication Details

Journal
Journal of Radiation Research and Applied Sciences
Published
2025-02-26
DOI
https://doi.org/10.1016/j.jrras.2025.101366
Citations
4
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
10.52
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article

A new discrete Burr III distribution for modeling radiation and biological data

Abdullah M. Alomair, Amara Javed, Muhammad Ahsan ul Haq, Muhammad Nasir Saddam Hussain
4 citations
Journal of Radiation Research and Applied Sciences
Statistical Distribution Estimation and Applications
10.52
article

A new discrete Burr III distribution for modeling radiation and biological data

Abdullah M. Alomair, Amara Javed, Muhammad Ahsan ul Haq, Muhammad Nasir Saddam Hussain
article en
4 citations

Abstract

A new three-parameter discrete probability distribution entitled “discrete modified Burr III” distribution is introduced using the survival discretization approach. We derived and explored various statistical properties such as moments, dispersion index, mean deviation, and order statistics. Additionally, we also explore its reliability characteristics including survival, hazard function, cumulative hazard function, and residual reliability function. The parameters of the model are estimated using the maximum likelihood estimation approach. A detailed Monte Carlo simulation is utilized to assess the performance of derived estimators across varying sample sizes. The utility of the new model is evaluated using biological and radiation datasets, demonstrating its better fit compared to considered competing discrete distribution. Additionally, we also analyzed these datasets using the Bayesian estimation approach.

Journal of Radiation Research and Applied SciencesVol. 18(2)
University of the Punjab (PK), Government College University, Lahore (PK), King Faisal University (SA)
Openalex Percentile: Top 2%
Statistical Distribution Estimation and Applications
10.52
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