A New Flexible Extension of Xgamma Distribution and its Application to COVID-19 Data

Abstract

In this article, a new flexible extension of xgamma probability distribution has been proposed. Several well known distributional properties viz., raw moments, generating functions, conditional moments, mean deviation, quantile functions etc., of this flexible extension model have derived and studied in detail. Further, the estimation of the unknown model parameters along with the survival function and hazard function are estimated using maximum likelihood estimation technique. The Monte Carlo simulation has been performed to check the consistency of the proposed estimators for the different variation of sample size and model parameters. Finally, the superiority of proposed extension over several well known lifetime models has been illustrated using four data sets pertaining to COVID-19 cases in different country of the world.