Open AccessNearly assumptionless screening for the mutually-exciting multivariate Hawkes process
Author(s) -
Shizhe Chen,
Daniela Witten,
Ali Shojaie
Publication year - 2017
Publication title -
electronic journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.482
H-Index - 54
ISSN - 1935-7524
DOI - 10.1214/17-ejs1251
Subject(s) - multivariate statistics , enhanced data rates for gsm evolution , simple (philosophy) , set (abstract data type) , graph , mathematics , process (computing) , computer science , algorithm , theoretical computer science , artificial intelligence , machine learning , mathematical optimization , philosophy , epistemology , programming language , operating system
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.