A Bivariate Generalized Exponential Distribution Derived From Copula Functions in the Presence of Censored Data and Covariates

Achcar, Jorge Alberto; Moala, Fernando Antônio; Tarumoto, Mário Hissamitsu; Coladello, Leandro Fernandes

doi:10.1590/0101-7438.2015.035.01.0165

Cited by 10 publications

(11 citation statements)

References 17 publications

(6 reference statements)

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“…Recently, Al-Turk et al (2017) discussed the inference procedures of the unknown parameters of a BGE distribution based on copula functions. Some of the other works related to BGE distribution can be found in Achcar et al (2015), Ibrahim et al (2017), Kundu and Gupta (2011), , Muhammad (2016), Shoaee and Khorram (2012) and the references cited therein.…”

Section: Bge Distributionsmentioning

confidence: 99%

On Bivariate Generalized Exponential-Power Series Class of Distributions

Jafari

Roozegar

2018

JIRSS

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Abstract. In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. This new class contains the bivariate generalized exponential-Poisson, bivariate generalized exponential-logarithmic, bivariate generalized exponential-binomial and bivariate generalized exponential-negative binomial distributions as special cases. We derive different properties of the proposed class of distributions. It is observed that the proposed class of bivariate distributions is a very flexible class of distribution functions. The joint probability density functions can have a variety of shapes. It can be bimodal as well as heavy tailed also. This distribution has five parameters. The maximum likelihood estimators of the parameters cannot be obtained in closed form. We propose to use EM algorithm to compute the maximum likelihood estimators of the unknown parameters. It is observed that the proposed EM algorithm can be implemented very easily in practice. One data-set has been analyzed for illustrative purposes. It is observed that the proposed model and the EM algorithm work quite well in practice.

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Section: Bge Distributionsmentioning

confidence: 99%

On Bivariate Generalized Exponential-Power Series Class of Distributions

Jafari

Roozegar

2018

JIRSS

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“…According to Achcar et al (2015), the joint bivariate survival function for the lifetimes 1 and 2 is given by Eq. 9…”

Section: Bivariate Exponentiated Pareto Distribution Derived From Gaumentioning

confidence: 99%

“…Hutchinson and Lai (1990) studied the existent of bivariate non-normal distributions and provided many applications for different bivariate models. The use of copula function give a great flexibility to derive bivariate lifetime distributions, see for example, AL-Hussaini and Ateya (2006), Quiroz-Flores (2009), Gupta et al (2010), Kundu andGupta (2011), El-Sherpieny et al (2013), Kundu (2015), Achcar et al (2015), and ElGohary and El-Morshedy (2015). The main aim of this paper is to introduce a bivariate exponentiated Pareto distribution derived from Gaussian copula with EP distribution as marginals.…”

Section: Introductionmentioning

confidence: 99%

A bivariate exponentiated Pareto distribution derived from Gaussian copula

Al-Urwi¹,

Baharith

2017

Int. j. adv. appl. sci.

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The exponentiated Pareto distribution has been used quite effectively to model many lifetime data. In this paper, a new bivariate exponentiated Pareto distribution is introduced. The proposed bivariate distribution is derived from Gaussian copula with exponentiated Pareto distribution as marginals. Some properties of the bivariate exponentiated Pareto distribution can be obtained using the Gaussian copula property. Moreover, several methods of estimation are considered to estimate the unknown parameters of the proposed bivariate distribution. Numerical simulations are carried out to compare the performances of different estimators. Finally, one real data is analyzed and the results showed that the proposed bivariate distribution is useful for real life applications.

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“…By using Metropolis-Hastings algorithm, since the conditional distributions in this case are not identified as known distributions, see Achcar et al [20].…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Sankaran and Kundu [19] discussed several other new properties for bivariate Pareto model such as the maximum likelihood estimator by using two stage estimator and analyzed two data sets for the bivariate Pareto Type II distribution. Achcar et al [20] introduced Bayesian analysis for a bivariate generalized exponential distribution with censored data from Copula functions and using MCMC methods to simulate samples. Dou et al [21] used order statistics to construct multivariate distributions with fixed marginals of the Bernstein copula in terms of a finite mixture distribution.…”

Section: Introductionmentioning

confidence: 99%

A bivariate Pareto type I models

Elaal

Alzahrani

2017

IJASP

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In this paper two new bivariate Pareto Type I distributions are introduced. The first distribution is based on copula, and the second distribution is based on mixture of and copula. Maximum likelihood and Bayesian estimations are used to estimate the parameters of the proposed distribution. A Monte Carlo Simulation study is carried out to study the behavior of the proposed distributions. A real data set is analyzed to illustrate the performance and flexibility of the proposed distributions.

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A Bivariate Generalized Exponential Distribution Derived From Copula Functions in the Presence of Censored Data and Covariates

Cited by 10 publications

References 17 publications

On Bivariate Generalized Exponential-Power Series Class of Distributions

On Bivariate Generalized Exponential-Power Series Class of Distributions

A bivariate exponentiated Pareto distribution derived from Gaussian copula

A bivariate Pareto type I models

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