具有发散顶点数的无向随机图的$β$ -模型的中心极限定理
A central limit theorem in the $β$-model for undirected random graphs with a diverging number of vertices
摘要：
Chatterjee，Diaconis and Sly（2011）最近建立了当顶点数趋于无穷大时$β$模型中最大似然估计的一致性。通过对Fisher信息矩阵的逆矩阵的近似，在较弱的条件下得到了它的渐近正态性.仿真研究和数据实例说明了理论结果。
英文摘要：
Chatterjee, Diaconis and Sly (2011) recently established the consistency of the maximum likelihood estimate in the $β$-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.
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