A central limit theorem in the $\beta$-model for undirected random graphs with a diverging number of vertices
Statistics Theory
2013-07-02 v3 Statistics Theory
Abstract
Chatterjee, Diaconis and Sly (2011) recently established the consistency of the maximum likelihood estimate in the -model when the number of vertices goes to infinity. By approximating the inverse of the Fisher information matrix, we obtain its asymptotic normality under mild conditions. Simulation studies and a data example illustrate the theoretical results.
Keywords
Cite
@article{arxiv.1202.3307,
title = {A central limit theorem in the $\beta$-model for undirected random graphs with a diverging number of vertices},
author = {Ting Yan and Jinfeng Xu},
journal= {arXiv preprint arXiv:1202.3307},
year = {2013}
}
Comments
6 pages. 2 tables