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A New Generalized-X Family for Analyzing the COVID-19 Data Set: a Case Study

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  • Yue Xin
  • Yinghui Zhou
  • Getachew Tekle Mekiso
  • Saima K Khosa

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.

Suggested Citation

  • Yue Xin & Yinghui Zhou & Getachew Tekle Mekiso & Saima K Khosa, 2022. "A New Generalized-X Family for Analyzing the COVID-19 Data Set: a Case Study," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, April.
    • Handle: RePEc:hin:jnlmpe:1901526
    DOI: 10.1155/2022/1901526
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