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.
Roverato A., Lupparelli M. , La Rocca L. (2013). Log-mean linear models for binary data. BIOMETRIKA, 100, 485-494 [10.1093/biomet/ass080].
Log-mean linear models for binary data
ROVERATO, ALBERTO;LUPPARELLI, MONIA;
2013
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.