In this paper we develop a bivariate discrete modifiedWeibull (BDMW) distribution and derived some of its important properties such as joint survival function, marginal survival function, conditional survival function, joint probability generating function and hazard rate function. The significance of additional parameter in BDMW is tested using generalized likelihood ratio test. Also we construct a finite mixture ofBDMWand established its identifiability condition. Certain properties of the mixture model are derived. The parameters of BDMW are estimated through method of maximum likelihood
In many real-world applications, the phenomena of interest are continuous in nature and modeled thro...
The Weibull distribution is one of the widely known lifetime distribution that has been extensively ...
There are not many known distributions for modeling discrete data. In this paper, we shall introduce...
In this paper, a new bivariate exponentiated modified Weibull distribution (BEMW) is introduced. It ...
This paper introduced a new BivariateModifiedWeibull (BMW) distribution. It is aMarshall-Olkin type....
In this paper, a new bivariate exponentiated modified Weibull extension distribution (BEMWE) is intr...
In many real-world applications, the random variables modeling the phenomena of interest are continu...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
A new class of multivariate distribution is derived where each component is a mixture of Weibull dis...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
A five-parameter distribution so-called the beta modified Weibull distribution is defined and studie...
A new class of multivariate discrete distributions with binomial and multinomial marginals is studie...
Modeling correlated count data through some bivariate (or multivariate) discrete distribution is ess...
In many real-world applications, the phenomena of interest are continuous in nature and modeled thro...
The Weibull distribution is one of the widely known lifetime distribution that has been extensively ...
There are not many known distributions for modeling discrete data. In this paper, we shall introduce...
In this paper, a new bivariate exponentiated modified Weibull distribution (BEMW) is introduced. It ...
This paper introduced a new BivariateModifiedWeibull (BMW) distribution. It is aMarshall-Olkin type....
In this paper, a new bivariate exponentiated modified Weibull extension distribution (BEMWE) is intr...
In many real-world applications, the random variables modeling the phenomena of interest are continu...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
A new class of multivariate distribution is derived where each component is a mixture of Weibull dis...
In many real-world problems, the phenomena of interest are continuous in nature and modeled through ...
A five-parameter distribution so-called the beta modified Weibull distribution is defined and studie...
A new class of multivariate discrete distributions with binomial and multinomial marginals is studie...
Modeling correlated count data through some bivariate (or multivariate) discrete distribution is ess...
In many real-world applications, the phenomena of interest are continuous in nature and modeled thro...
The Weibull distribution is one of the widely known lifetime distribution that has been extensively ...
There are not many known distributions for modeling discrete data. In this paper, we shall introduce...