Statistical Science

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Alex Reinhart

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Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. In this review, I describe the basic theory, survey related estimation and inference techniques from each field, highlight several key applications, and suggest directions for future research.

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Statist. Sci., Volume 33, Number 3 (2018), 299-318.

First available in Project Euclid: 13 August 2018

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Epidemic-Type Aftershock Sequence conditional intensity Hawkes process stochastic declustering


Reinhart, Alex. A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications. Statist. Sci. 33 (2018), no. 3, 299--318. doi:10.1214/17-STS629.

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