Inference for Preferential Attachment Models and Related Topics

dc.contributor.advisor	Vidyashankar, Anand N.
dc.contributor.author	Saxton, Daniel
dc.creator	Saxton, Daniel
dc.date.accessioned	2014-09-18T01:56:57Z
dc.date.available	2014-09-18T01:56:57Z
dc.date.issued	2014-05
dc.description.abstract	Preferential attachment models arise in several areas of mathematics and scientific applications such as in the analysis of social, financial, and gene regulatory networks. However, inferential questions related to such models are challenging and have so far not been addressed. In this dissertation, we provide a framework using branching processes within which to investigate these issues. In particular, we develop theory that may be employed to extract information about the strength of preferential attachment from graph data, as well as information about degree asymptotics. We also study an extension of the model incorporating random effects which helps to introduce added heterogeneity into the process which is not represented in existing models. Questions concerning cascades on trees are also studied.
dc.format.extent	66 pages
dc.identifier.uri	https://hdl.handle.net/1920/8917
dc.language.iso	en
dc.rights	Copyright 2014 Daniel Saxton
dc.subject	Statistics
dc.title	Inference for Preferential Attachment Models and Related Topics
dc.type	Dissertation
thesis.degree.discipline	Statistical Science
thesis.degree.grantor	George Mason University
thesis.degree.level	Doctoral

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