Graphical modeling for multivariate Hawkes processes with nonparametric link functions
Hawkes introduced a powerful multivariate point process model of mutually exciting processes to explain causal structure in data. In this article, it is shown that the Granger causality structure of such processes is fully encoded in the corresponding link functions of the model. A new nonparametric...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
Mar 2017
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| In: |
Journal of time series analysis
Year: 2017, Jahrgang: 38, Heft: 2, Pages: 225-242 |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/jtsa.12213 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1111/jtsa.12213
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| Verfasserangaben: | Michael Eichler, Rainer Dahlhaus and Johannes Dueck |
| Zusammenfassung: | Hawkes introduced a powerful multivariate point process model of mutually exciting processes to explain causal structure in data. In this article, it is shown that the Granger causality structure of such processes is fully encoded in the corresponding link functions of the model. A new nonparametric estimator of the link functions based on a time-discretized version of the point process is introduced by using an infinite order autoregression. Consistency of the new estimator is derived. The estimator is applied to simulated data and to neural spike train data from the spinal dorsal horn of a rat. |
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| Beschreibung: | Published online 13 September 2016 Gesehen am 08.05.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 1467-9892 |
| DOI: | 10.1111/jtsa.12213 |