Title:
A Self-limiting Hawkes Process: Interpretation, Estimation, and Use in Modeling

dc.contributor.advisor Short, Martin B.
dc.contributor.author Olinde, John Garnier
dc.contributor.committeeMember Kang, Sung Ha
dc.contributor.committeeMember Zhou, Haomin
dc.contributor.committeeMember Liao, Wenjing
dc.contributor.committeeMember Yan, Karen
dc.contributor.department Mathematics
dc.date.accessioned 2022-05-18T19:33:55Z
dc.date.available 2022-05-18T19:33:55Z
dc.date.created 2022-05
dc.date.issued 2022-04-28
dc.date.submitted May 2022
dc.date.updated 2022-05-18T19:33:55Z
dc.description.abstract Many real life processes that we would like to model have a self-exciting property, i.e. the occurrence of one event causes a temporary spike in the probability of other events occurring nearby in space and time. Examples of processes that have this property are earthquakes, crime in a neighborhood, or emails within a company. In 1971, Alan Hawkes first used what is now known as the Hawkes process to model such processes. Since then much work has been done on estimating the parameters of a Hawkes process given a data set and creating variants of the process for different applications. In this thesis, we propose a new variant of a Hawkes process, called a self-limiting Hawkes process, that takes into account the effect of police activity on the underlying crime rate and an algorithm for estimating its parameters given a crime data set. We show that the self-limiting Hawkes process fits real crime data just as well, if not better, than the standard Hawkes model.We also show that the self-limiting Hawkes process fits real financial data at least as well as the standard Hawkes model.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66584
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Hawkes
dc.subject Hawkes process
dc.subject mathematical modeling
dc.subject self-limiting Hawkes process
dc.title A Self-limiting Hawkes Process: Interpretation, Estimation, and Use in Modeling
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Short, Martin B.
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Mathematics
relation.isAdvisorOfPublication 92a6f1bd-3412-4ea8-98c4-87c984973eb6
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 84e5d930-8c17-4e24-96cc-63f5ab63da69
thesis.degree.level Doctoral
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
OLINDE-DISSERTATION-2022.pdf
Size:
881.09 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
3.86 KB
Format:
Plain Text
Description: