In this paper we further develop the theory of vertical density representation (VDR) in the multivariate case and provide a formula for the calculation of the conditional probability density of a random vector when its density value is given. An application to random vector generation is also given.Department of Applied Mathematic
We suggest two methods for simulating from a multivariate copula in an arbitrary dimension. Although...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgewort...
In this paper, a method called the vertical strip (VS) method is proposed for generating non-uniform...
AbstractIt is shown that the conditional probability density function of Y1 given (1n) Σi=1n Yi=1Yit...
AbstractA general real matrix-variate probability model is introduced here, which covers almost all ...
The thesis focuses on the distributions of random vectors in Cartesian, polar and directional coordi...
This text presents an overview on multivariate discrete distributions such as the multivariate Poiss...
A fairly general procedure is studied to perturbate a multivariate density satisfying a weak form of...
This paper introduces new ways to construct probability integral transforms of random vectors that c...
We present a construction principle for the spectral density of a multivariate extreme value distrib...
In this paper we present generalization of probability density of random variables. It is obvious th...
This thesis develops models and associated Bayesian inference methods for flexible univariate and mu...
An algorithm for generating correlated random variables with known marginal distributions and a spec...
This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgewort...
We suggest two methods for simulating from a multivariate copula in an arbitrary dimension. Although...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgewort...
In this paper, a method called the vertical strip (VS) method is proposed for generating non-uniform...
AbstractIt is shown that the conditional probability density function of Y1 given (1n) Σi=1n Yi=1Yit...
AbstractA general real matrix-variate probability model is introduced here, which covers almost all ...
The thesis focuses on the distributions of random vectors in Cartesian, polar and directional coordi...
This text presents an overview on multivariate discrete distributions such as the multivariate Poiss...
A fairly general procedure is studied to perturbate a multivariate density satisfying a weak form of...
This paper introduces new ways to construct probability integral transforms of random vectors that c...
We present a construction principle for the spectral density of a multivariate extreme value distrib...
In this paper we present generalization of probability density of random variables. It is obvious th...
This thesis develops models and associated Bayesian inference methods for flexible univariate and mu...
An algorithm for generating correlated random variables with known marginal distributions and a spec...
This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgewort...
We suggest two methods for simulating from a multivariate copula in an arbitrary dimension. Although...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgewort...