Sequential data assimilation for 1D self-exciting processes with application to urban crime data
type: Item
instance of: scholarly article
title: Sequential data assimilation for 1D self-exciting processes with application to urban crime data
zbMATH Open document ID: 1469.62138
published in: Computational Statistics and Data Analysis
publication date: 2018-10-17T00:00:00Z
full work available at URL: CSDA submitted.pdf
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 60G55
Mathematics Subject Classification ID: 62P25
zbMATH DE Number: 6957711
zbMATH Keywords: nonlinear filtering
zbMATH Keywords: Hawkes process
zbMATH Keywords: joint state-parameter estimation
zbMATH Keywords: count data
zbMATH Keywords: particle filtering
zbMATH Keywords: ensemble Kalman filter
author: Naratip Santitissadeekorn
author: Martin B. Short
author: David J. B. Lloyd
MaRDI profile type: Publication
OpenAlex ID: W2883237442
cites work: A survey of convergence results on particle filtering methods for practitioners
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cites work: Data Assimilation
cites work: Nonlinear data assimilation
cites work: Sequential Monte Carlo Methods for Dynamic Systems
cites work: Modeling and estimation of multi-source clustering in crime and security data
cites work: Self-Exciting Point Process Modeling of Crime
cites work: On Lewis' simulation method for point processes
cites work: Space and circular time log Gaussian Cox processes with application to crime event data
cites work: Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime
Wikidata QID: Q129508040
DOI: 10.1016/J.CSDA.2018.06.014
description: scientific article; zbMATH DE number 6957711