Open AccessExponentiated transformation of Gumbel Type-II distribution for modeling COVID-19 data
Author(s) -
Tabassum Naz Sindhu,
Anum Shafiq,
Qasem M. AlMdallal
Publication year - 2020
Publication title -
alexandria engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.584
H-Index - 58
eISSN - 2090-2670
pISSN - 1110-0168
DOI - 10.1016/j.aej.2020.09.060
Subject(s) - gumbel distribution , mathematics , hazard , transformation (genetics) , statistics , quantile , parametric statistics , parametric model , function (biology) , covid-19 , bayesian probability , econometrics , extreme value theory , medicine , biochemistry , chemistry , organic chemistry , disease , pathology , evolutionary biology , biology , infectious disease (medical specialty) , gene
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 Exponentiatedtransformation 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. Additionaly, 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.
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