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

Ting Yan; Jinfeng Xu

doi:10.1093/biomet/ass084

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

Ting Yan, Jinfeng Xu

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

70 Citations (Scopus)

Abstract

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. © 2013 Biometrika Trust.

Original language	English
Pages (from-to)	519-524
Journal	Biometrika
Volume	100
Issue number	2
DOIs	https://doi.org/10.1093/biomet/ass084
Publication status	Published - Jun 2013
Externally published	Yes

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

β-model
Central limit theorem
Fisher information matrix

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N2 - 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. © 2013 Biometrika Trust.

AB - 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. © 2013 Biometrika Trust.

KW - β-model

KW - Central limit theorem

KW - Fisher information matrix

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DO - 10.1093/biomet/ass084

M3 - RGC 21 - Publication in refereed journal

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VL - 100

SP - 519

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JO - Biometrika

JF - Biometrika

IS - 2

ER -

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

Abstract

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Research Keywords

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