Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels

Abstract

Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the literature.

Published in at http://dx.doi.org/10.1214/13-AOS1124 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• Stochastic blockmodels and community structure in networks
• Matrix estimation by Universal Singular Value Thresholding
• The method of moments and degree distributions for network models
• The Highest Dimensional Stochastic Blockmodel with a Regularized Estimator

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