Asymptotic distribution in affiliation finite discrete weighted networks with an increasing degree sequence

Jing Luo, Hong Qin and Zhenghong Wang

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 17, 4195-4205

Abstract: The asymptotic normality of a fixed number of the maximum likelihood estimators (MLEs) in the affiliation finite discrete weighted networks with an increasing 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 asymptotic results.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2018.1487984 (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:48:y:2019:i:17:p:4195-4205

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2018.1487984

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 ().