Open Access

ARTICLE

Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data

Amal S. Hassan¹, Ehab M. Almetwally^2,*, Gamal M. Ibrahim³

1 Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
2 Faculty of Business Administration, Delta University of Science and Technology, Mansoura, 35511, Egypt
3 High Institute for Management Sciences, Belqas, 35511, Egypt

* Corresponding Author: Ehab M. Almetwally. Email:

(This article belongs to the Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)

Computers, Materials & Continua 2021, 68(1), 337-358. https://doi.org/10.32604/cmc.2021.013971

Received 30 August 2020; Accepted 17 October 2020; Issue published 22 March 2021

Abstract

In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as; Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recently inverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leone distribution, is proposed by combining the Kumaraswamy-G family and the inverted Topp–Leone distribution. We initially provide a linear representation of its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferroni curves, entropies measures and stress-strength reliability. Then, Bayesian and maximum likelihood estimators for parameters of the Kumaraswamy inverted Topp–Leone distribution under Type-II censored sample are considered. Bayesian estimator is regarded using symmetric and asymmetric loss functions. As analytical solution is too hard, behaviours of estimates have been done viz Monte Carlo simulation study and some reasonable comparisons have been presented. The outcomes of the simulation study confirmed the efficiencies of obtained estimates as well as yielded the superiority of Bayesian estimate under adequate priors compared to the maximum likelihood estimate. Application to COVID-19 in some countries showed that the new distribution is more appropriate than some other competitive models.

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