March 5, 2020, 11:00–12:15
Toulouse
Room Auditorium 6
MAD-Stat. Seminar
Abstract
We propose a new class of estimators for semiparametric VARMA models with unspecified innovation density. Our estimators are based on the measure transportation-based concepts of multivariate center-outward ranks and signs. Root-$n$ consistency and asymptotic normality are obtained under a broad class of innovation densities including, e.g., multimodal mixtures of Gaussians. Simulations establish the impressive performances of the resulting R-estimators, which quite significantly outperform, under non-Gaussian and non-elliptical innovation densities, the routinely-applied Gaussian quasi-likelihood method.