Uniform decomposition of probability measures: quantization, clustering and rate of convergence

Julien Chevallier (1, 2)

1 AGM - UMR 8088 - Analyse, Géométrie et Modélisation
2 SVH - Statistique pour le Vivant et l’Homme

The study of finite approximations of probability measures has a long history. In (Xu and Berger, 2017), the authors focus on constrained finite approximations and, in particular, uniform ones in dimension d = 1. The present paper gives an elementary construction of a uniform decomposition of probability measures in dimension d ≥ 1. This decomposition is then used to give upper-bounds on the rate of convergence of the optimal uniform approximation error. These bounds appear to be the generalization of the ones obtained in (Xu and Berger, 2017) and to be sharp for generic probability measures.

Mots clés: Wasserstein distance; Rate of convergence; Uniform approximation; Clustering; Quantization

Domaines: Probabilités [math.PR]

Julien Chevallier. Uniform decomposition of probability measures: quantization, clustering and rate of convergence. Journal of Applied Probability, 2018, 55 (4), pp.1037-1045. ⟨10.1017/jpr.2018.69⟩. ⟨hal-01677877v2⟩