The Annals of Statistics

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

Peter Bickel, David Choi, Xiangyu Chang, and Hai Zhang

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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.

Article information

Source
Ann. Statist., Volume 41, Number 4 (2013), 1922-1943.

Dates
First available in Project Euclid: 23 October 2013

Permanent link to this document
https://projecteuclid.org/euclid.aos/1382547508

Digital Object Identifier
doi:10.1214/13-AOS1124

Mathematical Reviews number (MathSciNet)
MR3127853

Zentralblatt MATH identifier
1292.62042

Subjects
Primary: 62F12: Asymptotic properties of estimators

Keywords
Network statistics stochastic blockmodeling variational methods maximum likelihood

Citation

Bickel, Peter; Choi, David; Chang, Xiangyu; Zhang, Hai. Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann. Statist. 41 (2013), no. 4, 1922--1943. doi:10.1214/13-AOS1124. https://projecteuclid.org/euclid.aos/1382547508


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