On the analysis of number of deaths due to Covid −19 outbreak data using a new class of distributions

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

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature.

Original languageEnglish
Article number103747
JournalResults in Physics
Publication statusPublished - Feb 2021


  • Bayesian analysis
  • Entropies
  • Generalized log-exponential distribution
  • Gumbel type-II model
  • Stochastic order

ASJC Scopus subject areas

  • General Physics and Astronomy


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