Estimation of the Parameters in an Expanding Dynamic Network Model

Wei Zhao () and S.N. Lahiri
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Wei Zhao: North Carolina State University
S.N. Lahiri: Washington University in St Louis

Sankhya A: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 8, 282 pages

Abstract: Abstract In this paper, we consider an expanding sparse dynamic network model where the time evolution is governed by a Markovian structure. Transition of the network from time t to t + 1 involves three components where a new node joins the existing network, some of the existing edges drop out, and new edges are formed with the incoming node. We consider long term behavior of the network density and establish its limit. We also study asymptotic distributions of the maximum likelihood estimators of key model parameters. We report results from a simulation study to investigate finite sample properties of the estimators.

Keywords: Bootstrap; limit distribution; maximum likelihood estimators; network density.; Primary; 62E20 Secondary; 62M05 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13171-021-00258-z

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