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Abstract Details
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Activity Number:
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31
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301154 |
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Title:
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A Nonparametric EM Algorithm for a Multiscale Hawkes Process
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Author(s):
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Erik Lewis*+ and George Mohler
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Companies:
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University of California at Los Angeles and Santa Clara University
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Address:
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, , ,
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Keywords:
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Nonparametric ;
Expectation-Maximization ;
Maximum Penalized Likelihood Estimation ;
Point Process
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Abstract:
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Estimating the conditional intensity of a self-exciting point process can be difficult, especially when both exogenous and endogenous effects play a role in clustering. Improving on an expectation-maximization type algorithm propsed by Marsan and Lengline, we employ maximum penalized likelihood estimation to simultaneously estimate the background rate and the triggering density of Hawkes process intensities that vary over multiple time scales. We numerically investigate convergence rates of our algorithm for known examples and then use the model to examine self-excitation in Iraq IED event patterns.
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