Statistical Inference in a Directed Network Model With Covariates

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成果类型：

期刊论文

作者：

Yan, Ting;Jiang, Binyan;Fienberg, Stephen E.;Leng, Chenlei*

通讯作者：

Leng, Chenlei

作者机构：

[Yan, Ting] Cent China Normal Univ, Dept Stat, Wuhan, Hubei, Peoples R China.

[Jiang, Binyan] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Peoples R China.

[Fienberg, Stephen E.] Carnegie Mellon Univ, Machine Learning Dept, Cylab, Dept Stat,Heinz Coll, Pittsburgh, PA 15213 USA.

[Leng, Chenlei] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England.

[Leng, Chenlei] Alan Turing Inst, Coventry CV4 7AL, W Midlands, England.

通讯机构：

[Leng, Chenlei] U

[Leng, Chenlei] A

Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England.

Alan Turing Inst, Coventry CV4 7AL, W Midlands, England.

语种：

英文

关键词：

Asymptotic normality;Consistency;Degree heterogeneity;Homophily;Increasing number of parameters;Maximum likelihood estimator

期刊：

Journal of the American Statistical Association

ISSN：

0162-1459

年：

2019

卷：

114

期：

526

页码：

857-868

DOI：

10.1080/01621459.2018.1448829

基金类别：

Yan’s research is partially supported by the National Natural Science Foundation of China (No. 11771171). Jiang’s research is partially supported by the Hong Kong RGC grant (PolyU 253023/16P). Leng’s research is partially supported by a Turing Fellowship under the EPSRC grant EP/N510129/1.

机构署名：

本校为第一机构

院系归属：

数学与统计学学院

摘要：

Networks are often characterized by node heterogeneity for which nodes exhibit different degrees of interaction and link homophily for which nodes sharing common features tend to associate with each other. In this article, we rigorously study a directed network model that captures the former via node-specific parameterization and the latter by incorporating covariates. In particular, this model quantifies the extent of heterogeneity in terms of outgoingness and incomingness of each node by different parameters, thus allowing the number of heter...

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Statistical Inference in a Directed Network Model With Covariates

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