Log-mean linear models for binary data

• 成果类型: Article
• 署名作者: Roverato, A.; Lupparelli, M.; La Rocca, L.
• 署名单位: University of Bologna; Universita di Modena e Reggio Emilia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass080
• 发表日期: 2013
• 页码: 485494
• 关键词: graph models; parameterizations

摘要

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.