Electronic Journal of Statistics

Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process

Shizhe Chen, Daniela Witten, and Ali Shojaie

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We consider the task of learning the structure of the graph underlying a mutually-exciting multivariate Hawkes process in the high-dimensional setting. We propose a simple and computationally inexpensive edge screening approach. Under a subset of the assumptions required for penalized estimation approaches to recover the graph, this edge screening approach has the sure screening property: with high probability, the screened edge set is a superset of the true edge set. Furthermore, the screened edge set is relatively small. We illustrate the performance of this new edge screening approach in simulation studies.

Article information

Electron. J. Statist., Volume 11, Number 1 (2017), 1207-1234.

Received: September 2016
First available in Project Euclid: 11 April 2017

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60G55: Point processes
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62H12: Estimation

Hawkes process screening high-dimensionality

Creative Commons Attribution 4.0 International License.


Chen, Shizhe; Witten, Daniela; Shojaie, Ali. Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process. Electron. J. Statist. 11 (2017), no. 1, 1207--1234. doi:10.1214/17-EJS1251. https://projecteuclid.org/euclid.ejs/1491897620

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