Advancing Covid-19 Data Modeling: Introducing a Neutrosophic Extension of Ramous Louzada Distribution

Abstract

In this research, we introduce a neutrosopic extension of the Ramous Louzada Distribution called the Inverse Ramous Louzada Distribution. We delve into several mathematical properties of this distribution, including the Survival function, Hazard Rate function, cumulative Hazard Rate function, and estimation technique. Moreover, we conduct a comparative analysis between the Inverse Weibull distribution and the traditional Ramous Louzada Distribution, which are two widely used distributions. Our aim is to assess the performance of the developed model through Maximum Likelihood Estimation (MLE), Standard Error (SE), and Goodness of Fit tests.