Log-mean linear models for binary data
Methodology
2013-01-14 v4
Abstract
This paper introduces a novel class of models for binary data, which we call log-mean linear models. The characterizing feature of these models is that 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.
Keywords
Cite
@article{arxiv.1109.6239,
title = {Log-mean linear models for binary data},
author = {Alberto Roverato and Monia Lupparelli and Luca La Rocca},
journal= {arXiv preprint arXiv:1109.6239},
year = {2013}
}