On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models

Details On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models

2008-12-30

arxiv.org/abs/0901.0026v1

Statistics

Machine Learning

Scientific paper

There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with difficulties in computing maximum likelihood estimates. The issues associated with these difficulties relate to the broader structure of discrete exponential families. This paper re-examines the issues in two parts. First we consider the closure of $k$-dimensional exponential families of distribution with discrete base measure and polyhedral convex support $\mathrm{P}$. We show that the normal fan of $\mathrm{P}$ is a geometric object that plays a fundamental role in deriving the statistical and geometric properties of the corresponding extended exponential families. We discuss its relevance to maximum likelihood estimation, both from a theoretical and computational standpoint. Second, we apply our results to the analysis of ERG models. In particular, by means of a detailed example, we provide some characterization of the properties of ERG models, and, in particular, of certain behaviors of ERG models known as degeneracy.

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