Sequential data assimilation for 1D self-exciting processes with application to urban crime data

DOI10.1016/J.CSDA.2018.06.014zbMath1469.62138OpenAlexW2883237442WikidataQ129508040 ScholiaQ129508040MaRDI QIDQ1796944

Naratip Santitissadeekorn, David J. B. Lloyd, Martin B. Short

Publication date: 17 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://epubs.surrey.ac.uk/848620/1/CSDA_submitted.pdf

zbMATH Keywords

count datanonlinear filteringensemble Kalman filterparticle filteringHawkes processjoint state-parameter estimation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to social sciences (62P25) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (1)

Approximate filtering of conditional intensity process for Poisson count data: application to urban crime

Cites Work

• Modeling and estimation of multi-source clustering in crime and security data
• Nonlinear data assimilation
• Space and circular time log Gaussian Cox processes with application to crime event data
• Sequential Monte Carlo Methods in Practice
• On Lewis' simulation method for point processes
• Sequential Monte Carlo Methods for Dynamic Systems
• Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime
• Self-Exciting Point Process Modeling of Crime
• Data Assimilation
• A survey of convergence results on particle filtering methods for practitioners

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