Log-mean linear models for binary data

A. Roverato, Monia Lupparelli and L. La Rocca

Biometrika, 2013, vol. 100, issue 2, 485-494

Abstract: This paper introduces a novel class of models for binary data, which we call log-mean linear models. They are specified by linear constraints on the log-mean linear parameter, defined as a log-linear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain log-mean linear interactions to zero and, more specifically, that graphical models of marginal independence are log-mean linear models. Our approach overcomes some drawbacks of the existing parameterizations of graphical models of marginal independence. Copyright 2013, Oxford University Press.

Date: 2013
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