Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: $L^p$ and almost sure rates of convergence

Antoine Godichon-Baggioni (1)

1 IMB - Institut de Mathématiques de Bourgogne [Dijon]

The geometric median, also called $L^1$-median, is often used in robust statistics. Moreover, it is more and more usual to deal with large samples taking values in high dimensional spaces. In this context, a fast recursive estimator has been introduced by Cardot et al. (2013). This work aims at studying more precisely the asymptotic behavior of the estimators of the geometric median based on such non linear stochastic gradient algorithms. The $L^p$ rates of convergence as well as almost sure rates of convergence of these estimators are derived in general separable Hilbert spaces. Moreover, the optimal rates of convergence in quadratic mean of the averaged algorithm are also given.

Mots clés: Functional data analysis; Law of large numbers; Martingales in Hilbert space; Recursive estimation; Robust statistics; Spatial median; Stochastic gradient; Algorithms

Domaines: Statistiques [math.ST]

Antoine Godichon-Baggioni. Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: $L^p$ and almost sure rates of convergence. Journal of Multivariate Analysis, 2016, 146, pp.209 - 222. ⟨10.1016/j.jmva.2015.09.013⟩. ⟨hal-01413440⟩