The bivariate probit model is frequently used for estimating the effect of an endogenous binary regressor (the 'treatment') on a binary health outcome variable. This paper discusses simple modifications that maintain the probit assumption for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula bivariate probit model to the effect of ...

We define the class of bivariate binomial distributions as Poisson distributions transformed by a binomial dilution and mixing with a gamma distribution. They are used to study the number of nematodes, considering the sex ratio, parasitizing birds.

In this paper, we introduce the Morgenstern type bivariate generalized exponential distribution. This distribution is an extension of Morgenstern type bivariate exponential distribution and the marginal distributions are generalized exponential distribution. We study some properties of this bivariate distribution. Also, some distributional properties of concomitants of order statistics as well ...

Recently Bassan and Spizzichino (1999) have given some new concepts of multivariate aging for exchangeable random variables, such as a special type of bivariate IFR, by comparing distributions of residual lifetimes of dependent components of different ages. In the same vein, we further study some properties of concepts of IFR in the bivariate case. Then we introduce concepts of bivariate DMRL a...

The bivariate distribution with exponential conditionals (BEC) is introduced by Arnold and Strauss [Bivariate distributions with exponential conditionals, J. Amer. Statist. Assoc. 83 (1988) 522–527]. This work presents a simple and fast algorithm for simulating random variates from this density.

Many situations in real world cannot be described by a single variable. Simul taneous occurrence of multiple events warrants multivariate distributions. For instance, univariate geometric distribution can represent occurrence of failure of one component of a system. However, to study systems with several com ponents that may have different types of failures, such as twin engines of an airplane ...

This report reviews conjugate priors and priors closed under sampling for a variety of data generating processes where the prior distributions are univariate, bivariate, and multivariate. The effects of transformations on conjugate prior relationships are considered and cases where conjugate prior relationships can be applied under transformations are identified. Univariate and bivariate prior ...

Recently, Sarmanov’s family of bivariate distributions gained interest due to its flexible structure that easily joins given marginals. In this paper, we focus on the parameters estimation for the bivariate Sarmanov distribution with normal-type marginals. The maximum likelihood method is discussed and illustrated on both real and simulated data.

In this article we construct bivariate discrete distribution in Z. We make use of a generalized trivariate reduction technique. The special case leading to bivariate Skellam distributions is studied in detail. Properties of the derived models as well as estimation are examined. Real data application is provided. Discussion of extensions to different models is also mentioned.