A Bivariate Gamma Distribution Whose Marginals are Finite Mixtures of Gamma Distributions

Abstract

In this article a new bivariate distribution, whose both the marginals are finite mixture of gamma distribution has been defined. Several of its properties such moments, correlation coefficients, measure of skewness, moment generating function, Renyi and Shannon entropies have been derived. Simulation study have been conducted to evaluate the performance of maximum likelihood method.

The Marshall-Olkin Odd Exponential Half Logistic-G Family of Distributions: Properties and Applications

“…Some generalizations of the Marshall-Olkin distribution include: beta Marshall-Olkin-G (BMO-G) by Alizadeh et al [4], Kumaraswamy Marshall-Olkin-G (KwMO-G) by Alizadeh et al [5], Marshall-Olkin Log-logistic Extended Weibull (MOLLEW) family of distributions by Lepetu et al [19], Marshall-Olkin extended Weibull 480 THE MARSHALL-OLKIN ODD EXPONENTIAL HALF LOGISTIC-G FAMILY OF DISTRIBUTIONS family of distributions by Santos et al [29], Marshall-Olkin Kumaraswamy-G distribution by Chakraborty et al [9], Marshall-Olkin extended generalized Gompertz distribution by Lazhar et al [18] and Marshall-Olkin extended Burr Type III distribution by Kumar et al [16]. Other new generalizations available in the literature are bivariate gamma distribution by Rafiei et al [27], generalized modification of the Kumaraswamy distribution by Alshkaki [3] and Ristic-Balakrishnan odd log-logistic family of distributions by Esmaeili et al [10].…”

Investigating the Relationship between Processor and Memory Reliability in Data Science: A Bivariate Model Approach

“…In [8], the authors studied some families of bivariate Kumaraswamy distribution using different copulas. Others used the Marshall-Olkin bivariate copulas; one may refer to ( [9][10][11][12][13][14][15][16][17][18]). More recent work on bivariate models with copula functions with applications can be found in [19,20], among others.…”