A note on asymptotic distributions in directed exponential random graph models with bi-degree sequences

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成果类型：

期刊论文

作者：

Luo, Jing*;Qin, Hong;Yan, Ting;Zeyneb, Laala

通讯作者：

Luo, Jing

作者机构：

[Qin, Hong; Yan, Ting; Zeyneb, Laala; Luo, Jing] Cent China Normal Univ, Dept Stat, Wuhan 430079, Hubei, Peoples R China.

[Yan, Ting] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Hubei, Peoples R China.

通讯机构：

[Luo, Jing] C

Cent China Normal Univ, Dept Stat, Wuhan 430079, Hubei, Peoples R China.

语种：

英文

关键词：

Central limit theorem;Directed networks;Increasing number of parameters;Maximum likelihood estimator;62E20;62F12

期刊：

Communications in Statistics - Theory and Methods

ISSN：

0361-0926

年：

2017

卷：

期：

页码：

8852-8864

DOI：

10.1080/03610926.2016.1193202

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [11271147, 11471135, 11401239]; self-determined research funds of CCNU from the colleges's basic research and operation of MOE [CCNU15A02032, CCNU15ZD011]; KLAS [130026507]; Graduate student innovation fund of CCNU [2016CXZZ150]

机构署名：

本校为第一且通讯机构

院系归属：

数学与统计学学院

摘要：

The asymptotic normality of a fixed number of the maximum likelihood estimators (MLEs) in the directed exponential random graph models with an increasing bi-degree sequence has been established recently. In this article, we further derive a central limit theorem for a linear combination of all the MLEs with an increasing dimension. Simulation studies are provided to illustrate the asympt...

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A note on asymptotic distributions in directed exponential random graph models with bi-degree sequences

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