Enhanced Real-Life Data Modeling with the Modified Burr III Odds Ratio–G Distribution

Author:

Yang Haochong1,Huang Mingfang2ORCID,Chen Xinyu2,He Ziyan3,Pu Shusen2ORCID

Affiliation:

1. Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada

2. Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA

3. Department of Computer Science, University of Wisconsin-Madison, Madison, WI 53706, USA

Abstract

In this study, we introduce the modified Burr III Odds Ratio–G distribution, a novel statistical model that integrates the odds ratio concept with the foundational Burr III distribution. The spotlight of our investigation is cast on a key subclass within this innovative framework, designated as the Burr III Scaled Inverse Odds Ratio–G (B-SIOR-G) distribution. By effectively integrating the odds ratio with the Burr III distribution, this model enhances both flexibility and predictive accuracy. We delve into a thorough exploration of this distribution family’s mathematical and statistical properties, spanning hazard rate functions, quantile functions, moments, and additional features. Through rigorous simulation, we affirm the robustness of the B-SIOR-G model. The flexibility and practicality of the B-SIOR-G model are demonstrated through its application to four datasets, highlighting its enhanced efficacy over several well-established distributions.

Publisher

MDPI AG

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