
Multivariate Matrix Mittag–Leffler distributions
We extend the construction principle of multivariate phasetype distribu...
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Perfect TreeLike Markovian Distributions
We show that if a strictly positive joint probability distribution for a...
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Determining full conditional independence by loworder conditioning
A concentration graph associated with a random vector is an undirected g...
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Relative variation indexes for multivariate continuous distributions on [0,∞)^k and extensions
We introduce some new indexes to measure the departure of any multivaria...
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The Optimal 'AND'
The joint distribution P(X,Y) cannot be determined from its marginals P(...
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On truncated multivariate normal priors in constrained parameter spaces
We show that any lowerdimensional marginal density obtained from trunca...
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Conditional and marginal relative risk parameters for a class of recursive regression graph models
In linear regression modelling the distortion of effects after marginali...
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Multivariate binary probability distribution in the Grassmann formalism
We propose a probability distribution for multivariate binary random variables. For this purpose, we use the Grassmann number, an anticommuting number. In our model, the partition function, the central moment, and the marginal and conditional distributions are expressed analytically by the matrix of the parameters analogous to the covariance matrix in the multivariate Gaussian distribution. That is, summation over all possible states is not necessary for obtaining the partition function and various expected values, which is a problem with the conventional multivariate Bernoulli distribution. The proposed model has many similarities to the multivariate Gaussian distribution. For example, the marginal and conditional distributions are expressed by the parameter matrix and its inverse matrix, respectively. That is, the inverse matrix expresses a sort of partial correlation. Analytical expressions for the marginal and conditional distributions are also useful in generating random numbers for multivariate binary variables. Hence, we validated the proposed method using synthetic datasets. We observed that the sampling distributions of various statistics are consistent with the theoretical predictions and estimates are consistent and asymptotically normal.
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