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Common Drivers in Sparsely Interacting Hawkes Processes

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arxiv 2504.03916 v1 pith:EJJ2UW5T submitted 2025-04-04 math.ST stat.TH

classification math.STstat.TH
keywords commonmodelnetworkparametersactorsdrivershawkesactor
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We study a multivariate Hawkes process as a model for time-continuous relational event networks. The model does not assume the network to be known, it includes covariates, and it allows for both common drivers, parameters common to all the actors in the network, and also local parameters specific for each actor. We derive rates of convergence for all of the model parameters when both the number of actors and the time horizon tends to infinity. To prevent an exploding network, sparseness is assumed. We also discuss numerical aspects.

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    stat.ML 2025-09 conditional novelty 7.0 of 10

    Four permutation tests (ridge, group LASSO, and two CCA variants) detect association between node covariates and random-dot-product-graph latent structure, with consistency theorems and cheaper computation than prior ...

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