A central limit theorem in the β-model for undirected random graphs with a diverging number of vertices
成果类型：
Article
署名作者：
Yan, Ting; Xu, Jinfeng
署名单位：
Central China Normal University; New York University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/ass084
发表日期：
2013
页码：
519524
关键词：
摘要：
Chatterjee et al. (2011) established the consistency of the maximum likelihood estimator in the beta-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.