Improving Model Fitting for Applicable Medical Data: A Novel Exponentiated Transformation of BURR XII
This paper presents a novel extension of the Exponentiated Transformation of the BURR XII (ETBXII) distribution. In this paper, we explore the need for improved model fitting to applicable data and present a literature review on existing distributions that researchers modified. The proposed extension is introduced, and its properties, such as its hazard rate and distribution function, are addressed. The results demonstrate that the suggested extension can better match data than existing distributions and present an empirical application to demonstrate its utility. The conclusion of the manuscript discusses the potential effects of the suggested expansion on statistical modelling in several domains.
Authors
- Ali Algarni (ORCID: https://orcid.org/0000-0001-5994-1073)
Institutions
- King Abdulaziz University (SA)
Publication Details
- Journal
- Contemporary Mathematics
- Published
- 2025-03-18
- DOI
- https://doi.org/10.37256/cm.6220255666
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00