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

Sundus Naji Al-Aziz¹, Irsa Sajjad², Javid Gani Dar³, Abd Al-Aziz H. EL Bagoury^4,*

¹Department of Mathematical Sciences, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

 ²Department of mathematics and Statistics, Central South University, Changsha Hunan, China.

³Department of Applied Sciences, Symbiosis Institute of Technology, Symbiosis International (Deemed University) Pune, India

⁴Higher Istitute of Engineering and Technology, Elmahala Elkobra, Egypt

Emails: snalaziz@pnu.edu.sa; Irsasajjad@yahoo.com; javinfo.stat@yahoo.co.in; azizhel2013@yahoo.com

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.

Keywords: Survival Function; Hazard Rate Function; neutrosophic ramous Louzada Distribution; Maximum Likelihood Estimation.