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Statistical inference in a directed network model with covariates

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Yan, Ting, Jiang, Binyan, Fienberg, Stephen E. and Leng, Chenlei (2019) Statistical inference in a directed network model with covariates. Journal of the American Statistical Association, 114 (526). pp. 857-868. doi:10.1080/01621459.2018.1448829

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Official URL: https://doi.org/10.1080/01621459.2018.1448829

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Abstract

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 paper, we rigorously study a directed network model that captures the former via node-specific parametrization and the latter by incorporating covariates. In particular, this model quantities the extent of heterogeneity in terms of outgoingness and incomingness of each node by different parameters, thus allowing the number of heterogeneity parameters to be twice the number of nodes. We study the maximum likelihood estimation of the model and establish the uniform consistency and asymptotic normality of the resulting estimators. Numerical studies demonstrate our theoretical findings and two data analyses confirm the usefulness of our model.

Item Type: Journal Article
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Social networks -- Mathematical models, Communicable diseases -- Transmission -- Mathematical models
Journal or Publication Title: Journal of the American Statistical Association
Publisher: American Statistical Association
ISSN: 0162-1459
Official Date: 2019
Dates:
DateEvent
2019Published
14 March 2018Available
21 February 2018Accepted
Volume: 114
Number: 526
Page Range: pp. 857-868
DOI: 10.1080/01621459.2018.1448829
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
No. 11771171National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
PolyU 253023/16PResearch Grants Council, University Grants Committeehttp://dx.doi.org/10.13039/501100002920
EP/N510129/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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