Neutrosophic Quasi-XLindley distribution with applications of COVID-19 data

The Quasi-XLindley distribution (QXLD) is widely used in the field of survival and reliability engineering to simulate lifespan data in different fields of human, electronic designs and other fields. However, when dealing with uncertain data, a more generalized version of this distribution is needed. To address this, a neutrosophic Quasi-XLindley distribution (NQXLD) is developed in this paper. The NQXLD is particularly useful for representing skewed uncertain data. In this study, we present some statistical characteristics of the NQXL distribution, including the neutrosophic mean time failure, neutrosophic hazard rate, neutrosophic moments, and neutrosophic survival function. We also evaluate the parameters using the maximum likelihood (ML) estimation technique in a neutrosophic context based on a simulation study. Finally, applications of three different real data sets are considered to investigate the applicability of the suggested NQXL distribution. The results show the flexibility of the NQXL distribution in fitting various types of COVID-19 data as compared to the QXLD.