Construct · 2 names · 2 citations
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A self-exciting point process is a model for events in time whose instantaneous rate (conditional intensity) jumps up after each event and then decays, so that past events raise the probability of future ones and activity arrives in clusters rather than independently. Hawkes's 1971 Spectra of some self-exciting and mutually exciting point processes gave the construct its enduring mathematical form, and in statistics it is usually called the Hawkes process. The referent is a stochastic process defined by a baseline rate plus a triggering kernel, fitted to any clustered event stream.[1]
Seismologists use exactly the same construct under a different name: the epidemic-type aftershock sequence (ETAS) model treats every earthquake, of any size, as capable of triggering its own aftershocks, so the seismicity rate is a background rate plus the decaying influence of all past events. Ogata's 1988 Statistical models for earthquake occurrences and residual analysis for point processes built ETAS as a self-exciting point process with an Omori-type triggering kernel. It is not a different model from the statistician's Hawkes process but the same latent thing wearing a seismological name, a jangle in which one construct travels as 'Hawkes process' in one literature and 'ETAS' in another.[2]
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