A New Generalized-X Family for Analyzing the COVID-19 Data Set: a Case Study - Yue Xin (Mathematical Problems in Engineering, 2022)
Nowadays, researchers in applied sectors are highly motivated to propose and study new generalizations of the existing distributions to provide the best fit to data. To provide a close fit to data in numerous sectors, a series of new distributions have been proposed. In this study, we propose a new family called the new generalized-X (for short, “NG-X”) family of distributions. Based on the NG-X method, a novel modification of the Weibull model called the new generalized-Weibull (for short, “NG-Weibull”) distribution is studied. The heavy-tailed characteristics of the NG-X distributions are derived. The maximum likelihood estimators of the NG-X distributions are also obtained. Based on the special case (i.e., NG-Weibull) of the NG-X family, a simulation study is provided. The practical performance of the new NG-Weibull model is assessed by analyzing the COVID-19 data set. The fitting results of the NG-Weibull model are compared with three other competing models. Based on certain statistical measures, it is observed that the NG-Weibull model is the best competitive model.