A Novel Flexible Exponentiated XLindley Distribution with applications to Modeling COVID-19 Mortality and Precipitation

Abstract

Probability models play an important role in modeling the real-life data, particularly, modeling complex nature data. The COVID-19 pandemic has resulted in a significant increase in mortality rates globally. In this study we propose a new probability model to address the complex nature of such data sets. The new model is termed as Exponentiated- Exponentiated XLindley (EEXL) distribution. We present some key statistical properties of this distribution including moments, generating functions, order statistics etc. Parameters of the model are estimated using Maximum Likelihood Estimation (MLE). The performance of the model is evaluated using Netherlands COVID-19 data covering monthly mortality rate for 30 days (31^st march to April 30, 2020). Further, the model is also fitted to the precipitation data. We provide insights the distribution performance through simulation study. The proposed model provides efficient results as compared to well-known competitive distributions.