Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
Peter Bickel, David Choi, Xiangyu Chang, Hai Zhang
The Annals of Statistics · 2013 · Volume 41 · Issue 4
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.
Keywords
62F12, maximum likelihood, Network statistics, stochastic blockmodeling, variational methods
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