A central limit theorem in the β-model for undirected random graphs with a diverging number of vertices

Chatterjee et al. (2011) established the consistency of the maximum likelihood estimator in the β-model for undirected random graphs when the number of vertices goes to infinity. By approximating the inverse of the Fisher information matrix, we prove asymptotic normality of the maximum likelihood estimator under mild conditions. Simulation studies and a data example illustrate the theoretical results. Copyright 2013, Oxford University Press.

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Year of publication:	2013
Authors:	Yan, Ting ; Xu, Jinfeng
Published in:	Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 100.2013, 2, p. 519-524
Publisher:	Biometrika Trust

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Extent:	application/pdf
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010683217