IRUS Total

Parameter estimation of binned Hawkes processes

File Description SizeFormat 
Parameter Estimation of Binned Hawkes Processes.pdfPublished version1.8 MBAdobe PDFView/Open
Title: Parameter estimation of binned Hawkes processes
Authors: Shlomovich, L
Cohen, E
Adams, N
Patel, L
Item Type: Journal Article
Abstract: A key difficulty that arises from real event data is imprecision in the recording of event time-stamps. In many cases, retaining event times with a high precision is expensive due to the sheer volume of activity. Combined with practical limits on the accuracy of measurements, binned data is common. In order to use point processes to model such event data, tools for handling parameter estimation are essential. Here we consider parameter estimation of the Hawkes process, a type of self-exciting point process that has found application in the modeling of financial stock markets, earthquakes and social media cascades. We develop a novel optimization approach to parameter estimation of binned Hawkes processes using a modified Expectation-Maximization algorithm, referred to as Binned Hawkes Expectation Maximization (BH-EM). Through a detailed simulation study, we demonstrate that existing methods are capable of producing severely biased and highly variable parameter estimates and that our novel BH-EM method significantly outperforms them in all studied circumstances. We further illustrate the performance on network flow (NetFlow) data between devices in a real large-scale computer network, to characterize triggering behavior. These results highlight the importance of correct handling of binned data.
Issue Date: 11-Apr-2022
Date of Acceptance: 25-Feb-2022
URI: http://hdl.handle.net/10044/1/95850
DOI: 10.1080/10618600.2022.2050247
ISSN: 1061-8600
Publisher: American Statistical Association
Start Page: 990
End Page: 1000
Journal / Book Title: Journal of Computational and Graphical Statistics
Volume: 31
Issue: 4
Copyright Statement: © 2022 The Author(s). Published with license by Taylor and Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Publication Status: Published
Online Publication Date: 2022-03-16
Appears in Collections:Statistics
Faculty of Natural Sciences

This item is licensed under a Creative Commons License Creative Commons