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Activity Number: 368 - Recent Advances in Statistical Network Analysis with Applications
Type: Invited
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Graphics
Abstract #320686
Title: Nonparametric Inference Under a Birth-Death Dynamic Network Model
Author(s): Soumendra Lahiri*
Companies: Washington University in St Louis
Keywords: Random Graph; Dynamic Network; Nonparametric Inference

We consider a dynamic network model where the evolution of the network is governed by a Markovian structure. The network at t+1 evolves from the network at time point t via addition/birth of a random number of new nodes and the deletion/death of a random number of existing nodes. Long term network density depends on the probability parameters associated with the birth and the death events. We consider the maximum likelihood estimators of the parameters and obtain their limit distributions. We apply the result to a few selected real datasets.

Authors who are presenting talks have a * after their name.

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