Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters

作者：Yan, Ting^*; Zhao, Yunpeng; Qin, Hong

来源：Journal of Multivariate Analysis, 2015, 133: 61-76.

DOI：10.1016/j.jmva.2014.08.013

• Maximum entropy models
• Maximum likelihood estimator
• Asymptotic normality
• Increasing number of parameters

Maximum entropy models, motivated by applications in neuron science, are natural generalizations of the beta-model to weighted graphs. Similar to the beta-model, each vertex in maximum entropy models is assigned a potential parameter, and the degree sequence is the natural sufficient statistic. Hillar and Wibisono (2013) have proved the consistency of the maximum likelihood estimators. In this paper, we further establish the asymptotic normality for any finite number of the maximum likelihood estimators in the maximum entropy models with three types of edge weights, when the total number of parameters goes to infinity. Simulation studies are provided to illustrate the asymptotic results.

• 出版日期2015-1
• 单位华中师范大学