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

doi:10.1016/j.rinp.2020.103747

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

Results Phys. 2021 Feb:21:103747. doi: 10.1016/j.rinp.2020.103747. Epub 2020 Dec 29.

Authors

Tabassum Naz Sindhu^{1

2}, Anum Shafiq³, Qasem M Al-Mdallal⁴

Affiliations

¹ Department of Statistics, Quaid-i-Azam University 45320, Islamabad 44000, Pakistan.
² Department of Sciences and Humanities, FAST - National University, Islamabad, Pakistan.
³ School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
⁴ Department of Mathematical Sciences, UAE University, P.O. Box 15551, Al-Ain, United Arab Emirates.

Abstract

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.

Keywords: Bayesian analysis; Entropies; Generalized log-exponential distribution; Gumbel type-II model; Stochastic order.