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Asymptotic theory in network models with covariates and a growing number of node parameters

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成果类型:
期刊论文
作者:
Wang, Qiuping;Zhang, Yuan;Yan, Ting
通讯作者:
Yan, Ting(tingyanty@mail.ccnu.edu.cn)
作者机构:
[Wang, Qiuping] Zhaoqing Univ, Sch Math & Stat, Zhaoqing Ave, Zhaoqing 526061, Guangdong, Peoples R China.
[Zhang, Yuan] Ohio State Univ, Dept Stat, 1958 Neil Ave, Columbus, OH 43210 USA.
[Yan, Ting] Cent China Normal Univ, Dept Stat, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Ting Yan] D
Department of Statistics, Central China Normal University, Wuhan, China
语种:
英文
关键词:
\(\beta\)-Model;Degree heterogeneity;Network homophily;Network method of moments
期刊:
Annals of the Institute of Statistical Mathematics
ISSN:
0020-3157
年:
2023
卷:
75
期:
2
页码:
369-392
基金类别:
We are grateful to Editor, Associate Editor and two anonymous referees for their insightful comments and suggestions. TY was partially supported by the National Natural Science Foundation of China (Nos. 11771171, 12171188) and the Fundamental Research Funds for the Central Universities.
机构署名:
本校为其他机构
院系归属:
数学与统计学学院
摘要:
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment estimation method using node degrees and homophily statistics.We establish consistency and asymptotic normality of our estimator using novel analysis. We apply our general framework to three applications, including both exponential family and non-exponential family models. Comprehensive numerical studies and a data example also demonstrate the usefulness of our metho...

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