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- W3138633577 abstract "The exponentiated Pareto distribution has been used quite effectively to model many lifetime data. Constructing and studying bivariate probability distributions are of great interests of many statisticians. A popular and flexible way to derive different bivariate lifetime distributions using copula functions.In this Thesis, two new bivariate exponentiated Pareto distributions are introduced. The first proposed bivariate distribution is constructed based on Gaussian copula with exponentiated Pareto distribution as marginals and the second bivariate distribution is constructed based on M mixture representation and Gaussian copula. Several properties of the proposed bivariate distributions can be obtained using the Gaussian copula property. Different methods of estimation of the unknown parameters of proposed bivariate distributions are considered. The Markov Chain Monte Carlo technique has been used to compute the Bayesian estimates based on squared error loss function. Moreover, Monte Carlo simulation study is used to investigate and compare the different estimates for different sample sizes and for different values of the Gaussian copula parameter. Simulation results showed that Bayesian method in most cases provides more accurate estimates compared to other methods. In addition, the results based on mean square error showed that second bivariate distribution provides more accurate estimates compared to the first bivariate distribution. Finally, real data set is analyzed and the results showed that the proposed distributions gave more satisfactory performance compared to some other very well-known distributions." @default.
- W3138633577 created "2021-03-29" @default.
- W3138633577 creator A5073609499 @default.
- W3138633577 date "2016-01-01" @default.
- W3138633577 modified "2023-09-24" @default.
- W3138633577 title "Bivariate Exponentiated Pareto Distributions Based on Mixture and Copula" @default.
- W3138633577 hasPublicationYear "2016" @default.
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