Triangular systems for symmetric binary variables
Nanny Wermuth, Giovanni M. Marchetti, and D.R. Cox
Source: Electron. J. Statist. Volume 3
(2009), 932-955.
Abstract
We introduce and study distributions of sets of binary variables that are symmetric, that is each has equally probable levels. The joint distribution of these special types of binary variables, if generated by a recursive process of linear main effects is essentially parametrized in terms of marginal correlations. This contrasts with the log-linear formulation of joint probabilities in which parameters measure conditional associations given all remaining variables. The new formulation permits useful comparisons of different types of graphical Markov models and leads to a close approximation of Gaussian orthant probabilities.
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Keywords: Graphical Markov models; linear in probability models; log-linear models; recursive generating processes
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Mathematical Reviews number (MathSciNet): MR2540847
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