COMPSTAT 2016: Start Registration
View Submission - CRoNoS FDA 2016
A0152
Title: Lasso estimation of local independence graphs based on Hawkes processes Authors:  Vincent Rivoirard - Paris Dauphine University (France) [presenting]
Abstract: Functional connectivity in neuroscience is considered as one of the main features of the neural code. It is nowadays possible to obtain the spike activities of tens to hundreds of neurons simultaneously and the issue is then to infer the functional connectivity thanks to those complex data. To deal with this problem, we consider estimation of sparse local independence graphs by using models based on multivariate Hawkes processes. Such popular counting processes depend on an unknown functional parameter to be estimated by linear combinations of a fixed dictionary. To select coefficients, we propose a Lasso-type procedure, where data-driven weights of the $\ell_1$-penalty are derived from Bernstein inequalities. Our tuning procedure is proven to be robust with respect to all the parameters of the problem, revealing its potential for concrete purposes.