A note on undirected random graph models parameterized by the strengths of vertices
Qiuping Wang
Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 22, 5380-5398
Abstract:
The consistency and asymptotic normality of the moment estimator in a class of the so-called node-parameter network models have been established under the edge independence assumption. In this note, we extend the results to edge dependence case. We consider the marginal distribution of the network edge parameterized by a set of node parameters with possibly complex dependent structures. We present the moment estimation for inferring the node parameters. We obtain the consistency of the moment estimator of the node parameter under some mild conditions. The asymptotic representation of the moment estimator is also derived, which can be used to characterize its limiting distribution. Two applications are provided to illustrate the theoretical results.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1728332 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:50:y:2021:i:22:p:5380-5398
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2020.1728332
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().