A Parametric Regression Model using Power Chris-Jerry Distribution with Application to Censored Data

Section: Research Paper
Issue
Vol. 21 No. 1 (2024): Volume 21 Issue 1
Published
Jun 25, 2025
Pages
76-89

Abstract

The interdependency of various areas of Statistics is gaining good attention in the literature. This is possible through innovations such as the log-transformation of distribution to a parametric regression model hence integrating contemporary probability distribution models with the classical regression method. The new model is essentially preferred due to its applicability in wider scenarios. In this article, the base distribution is the Power Chris-Jerry distribution. The reparametrized regression model equivalent was carefully derived with the maximum estimation procedure under censored sample also considered. A simulation study of the log-power Chris-Jerry regression model was carried out with measures of performance presented. The COVID-19 patient lifetime censored data was deployed to justify the motivation for developing the new model and twelve competing models were used to compare the proposed regression. The results show that the proposed model is indeed better and preferred to its competitors.

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Authors

Christiana I. Ezeilo

Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

Iheanyi S. Onyeagu

Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

Uzoma E. Umeh

Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

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How to Cite

I. Ezeilo, C., S. Onyeagu, I., & E. Umeh, U. (2025). A Parametric Regression Model using Power Chris-Jerry Distribution with Application to Censored Data. IRAQI JOURNAL OF STATISTICAL SCIENCES, 21(1), 76–89. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/20599