2015
DOI: 10.1590/0101-7438.2015.035.01.0165
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: In this paper, we introduce a Bayesian analysis for a bivariate generalized exponential distribution in the presence of censored data and covariates derived from Copula functions. The generalized exponential distribution could be a good alternative to analyze lifetime data in comparison to usual existing parametric lifetime distributions as Weibull or Gamma distributions. We have being using standard existing MCMC (Markov Chain Monte Carlo) methods to simulate samples for the joint posterior of interest. Two e… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
11
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 12 publications
(16 citation statements)
references
References 17 publications
(6 reference statements)
0
11
0
Order By: Relevance
“…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%
“…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%
“…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%
“…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%