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Inference for Preferential Attachment Models and Related Topics

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dc.contributor.advisor Vidyashankar, Anand N.
dc.contributor.author Saxton, Daniel
dc.creator Saxton, Daniel en_US
dc.date.accessioned 2014-09-18T01:56:57Z
dc.date.available 2014-09-18T01:56:57Z
dc.date.issued 2014-05 en_US
dc.identifier.uri https://hdl.handle.net/1920/8917
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 en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Daniel Saxton en_US
dc.subject Statistics en_US
dc.title Inference for Preferential Attachment Models and Related Topics en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Statistical Science en
thesis.degree.grantor George Mason University en


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