Robust Estimation through Schoenberg transformations

Abstract

Schoenberg transformations, mapping Euclidean configurations into Euclidean configurations, define in turn a transformed inertia, whose minimization produces robust location estimates. The procedure only depends upon Euclidean distances between observations, and applies equivalently to univariate and multivariate data. The choice of the family of transformations and their parameters defines a flexible location strategy, generalizing M-estimators. Two regimes of solutions are identified. Theoretical results on their existence and stability are provided, and illustrated on two data sets.