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Activity Number: 356
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306921
Title: Comparison Between Maximum Likelihood Estimation and Maximum Pseudo-Likelihood Estimation for Exponential Random Graph Models
Author(s): Xiaolin Yang*+ and Alessandro Rinaldo and Stephen Fienberg
Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
Address: Baker Hall 132, Pittsburgh, PA, 15213,
Keywords: Exponential Random Graph Models ; Maximum Likelihood Estimation ; Maximum Pseudo-likelihood Estimation ; Degeneracy

Exponential Random Graph Models(ERGMs) are widely used for describing network data. As with other exponential family settings, the normalizing constant is rarely available in close form and its computation, which requires enumerating all possible graphs, makes the parameter estimation intractable for large networks. One way to deal with this problem is to approximate the likelihood function by a tractable function that is simpler to maximize, e.g., by using the method of pseudo-likelihood estimation suggested in the early network literature. In this paper, we compare maximum likelihood and maximum pseudo-likelihood estimation schemes with respect to existence and related degeneracy properties. We show that they differ in a number of important aspects.

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