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August 2013 Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
Peter Bickel, David Choi, Xiangyu Chang, Hai Zhang
Ann. Statist. 41(4): 1922-1943 (August 2013). DOI: 10.1214/13-AOS1124

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.

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Peter Bickel. David Choi. Xiangyu Chang. Hai Zhang. "Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels." Ann. Statist. 41 (4) 1922 - 1943, August 2013. https://doi.org/10.1214/13-AOS1124

Information

Published: August 2013
First available in Project Euclid: 23 October 2013

zbMATH: 1292.62042
MathSciNet: MR3127853
Digital Object Identifier: 10.1214/13-AOS1124

Subjects:
Primary: 62F12

Keywords: maximum likelihood , Network statistics , stochastic blockmodeling , variational methods

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.41 • No. 4 • August 2013
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