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Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data
- Source :
- Computers, Materials & Continua. 68:337-358
- Publication Year :
- 2021
- Publisher :
- Computers, Materials and Continua (Tech Science Press), 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
- Subjects :
- Bayes estimator
Bayesian probability
Monte Carlo method
Estimator
Probability density function
Quantile function
Computer Science Applications
Biomaterials
symbols.namesake
Bonferroni correction
Mechanics of Materials
Modeling and Simulation
Statistics
Prior probability
symbols
Electrical and Electronic Engineering
Mathematics
Subjects
Details
- ISSN :
- 15462226
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Computers, Materials & Continua
- Accession number :
- edsair.doi...........03280e3ab23ea2ed570e66aeaa8f88fe
- Full Text :
- https://doi.org/10.32604/cmc.2021.013971