A New Generalized-X Family for Analyzing the COVID-19 Data Set: a Case Study. (28th April 2022)

• Record Type: Journal Article
• Authors: Xin, Yue; Zhou, Yinghui; Mekiso, Getachew Tekle
• Other Names: K Khosa Saima Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-04-28
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/1901526
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.