Exponentiated transformation of Gumbel Type-II distribution for modeling COVID-19 data

Tabassum Naz Sindhu, Anum Shafiq, Qasem M. Al-Mdallal

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)

Abstract

The aim of this study is to analyze the number of deaths due to COVID-19 for Europe and China. For this purpose, we proposed a novel three parametric model named as Exponentiated transformation of Gumbel Type-II (ETGT-II) for modeling the two data sets of death cases due to COVID-19. Specific statistical attributes are derived and analyzed along with moments and associated measures, moments generating functions, uncertainty measures, complete/incomplete moments, survival function, quantile function and hazard function, etc. Additionally, model parameters are estimated by utilizing maximum likelihood method and Bayesian paradigm. To examine efficiency of the ETGT-II model a simulation analysis is performed. Finally, using the data sets of death cases of COVID-19 of Europe and China to show adaptability of suggested model. The results reveal that it may fit better than other well-known models.

Original languageEnglish
Pages (from-to)671-689
Number of pages19
JournalAlexandria Engineering Journal
Volume60
Issue number1
DOIs
Publication statusPublished - Feb 2021

Keywords

  • COVID-19
  • Entropies
  • Gumbel model
  • Reliability analysis
  • Stochastic order
  • Stress-strength analysis

ASJC Scopus subject areas

  • General Engineering

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