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

By Ting Yan, Jinfeng Xu. 2012

107 citations recorded by the source corpus.

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.

Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b0ed24fdef94c869a319460f49ed7cc8bf2498cf

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In Mathematical Physics

1. Avanti Athreya

In Statistical and Nonlinear Physics

1. Vince Lyzinski
2. Avanti Athreya
3. Daniel Lewis Sussman
4. Carey E. Priebe
5. Minh Tang
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In Statistics and Probability

1. Carey E. Priebe
2. Minh Tang
3. Vince Lyzinski
4. Daniel Lewis Sussman
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6. Hiroyuki Kasahara
7. Katsumi Shimotsu
8. Jiahua Chen

Citations in the corpus

• A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs (2013, background): “Finally, Yan and Xu (2013) provides a proof of the asymptotic normality for maximum likelihood estimates of the parameters of a related latent position model, the logistic β-model, and gives a derivation of the associated Fisher information matrix in that case.”