Modified Sarhan–Balakrishnan singular bivariate distribution

Kundu, Debasis; Gupta, Rajeev

doi:10.1016/j.jspi.2009.07.026

Cited by 37 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Post' and is available in Csörgö and Welsh (1989) Kundu and Gupta (2010) Jamalizadeh and Kundu (2013), and Balakrishna and Shiji (2014) analyzed this data. We divided all the data by 100.…”

Section: Example 2 the Data Set Was First Published In 'Washingtonmentioning

confidence: 99%

On Bivariate Exponentiated Extended Weibull Family of Distributions

Roozegar

Jafari

2016

CeN

View full text Add to dashboard Cite

show abstract

Section: Example 2 the Data Set Was First Published In 'Washingtonmentioning

confidence: 99%

On Bivariate Exponentiated Extended Weibull Family of Distributions

Roozegar

Jafari

2016

CeN

View full text Add to dashboard Cite

show abstract

“…Following the same steps as in Theorem 2.2 in [17], one can explicitly obtain the first two terms in last equation and after some algebra get g 0 (x) expression.…”

Section: Bayesian Data Analysis With Emo Modelsmentioning

confidence: 99%

“…The bivariate version of (6.16) for the joint distribution function is given by 17) where the function Using the above notations and relations, we list properties of d-EMO models which are not necessarily exchangeable. The statements are analogous to the corresponding properties of EMO models obtained in Sect.…”

Section: Model Specification and Basic Probabilistic Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Extended Marshall–Olkin Model and Its Dual Version

Pinto

Kolev

2015

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

We propose an extension of the generalized bivariate Marshall-Olkin model assuming dependence between the random variables involved. Probabilistic, aging properties, and survival copula representation of the extended model are obtained and illustrated by examples. Bayesian analysis is performed and possible applications are discussed. A dual version of extended Marshall-Olkin model is introduced and related stochastic order comparisons are presented. IntroductionA variety of bivariate (multivariate) extensions of the univariate exponential distribution have been considered in the literature. These include the distributions of [6,10,11,24], see a full review in [2].The vector (X 1 , X 2 ) meets the Marshall-Olkin model (MO hereafter) whenever it admits the stochastic representationwhere the random variables T i are independent and exponentially distributed with parameters λ i > 0, respectively, i = 1, 2, 3. The random variables T 1 and T 2 in (6.1) can be interpreted as the arrival time of individual shocks for two different components, while T 3 represents the time of arrival of a shock common to both components. Several applications of (6.1) can be mentioned. In reliability theory it can describe the lifetime of a system of two components operating in a random environment and subject to three independent

show abstract

“…For example, Sarhan and Balakrishnan [10] suggested a bivariate distribution that is more flexible than the bivariate exponential distribution. Later, this distribution was modified by Kundu and Gupta [11]. Kundu and Gupta [11,12] introduced the bivariate generalized exponential and bivariate proportional reversed hazard distributions, respectively, and argued its different properties.…”

Section: Introductionmentioning

confidence: 99%

Bivariate Mixture of Inverse Weibull Distribution: Properties and Estimation

Al-Moisheer

Alotaibi

Alomani

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this study, we construct a mixture of bivariate inverse Weibull distribution. We assumed that the parameters of two marginals have Bernoulli distributions. Several properties of the proposed model are obtained, such as probability marginal density function, probability marginal cumulative function, the product moment, the moment of the two variables x and y, the joint momentgenerating function, and the correlation between x and y. e real dataset has been analyzed. We observed that the mixture bivariate inverse Weibull distribution provides a better fit than the other model.

show abstract

Modified Sarhan–Balakrishnan singular bivariate distribution

Cited by 37 publications

References 15 publications

On Bivariate Exponentiated Extended Weibull Family of Distributions

On Bivariate Exponentiated Extended Weibull Family of Distributions

Extended Marshall–Olkin Model and Its Dual Version

Bivariate Mixture of Inverse Weibull Distribution: Properties and Estimation

Contact Info

Product

Resources

About