Inférence combinatoire en analyse géométrique des données

Abstract

In this paper, we aim at showing how, in Geometric Data Analysis (Correspondence Analysis, Principal Component Analysis...) descriptive statistics utilized as aids to interpretation can be used as combinatorial inference procedures based on permutation tests interpreted in terms of proportion of samples which are more extreme than the data. These procedures directly extend statistical description. In the first part, we will present typicality and homogeneity tests. In the second part, we will apply them to the principal variables provided by Multiple Correspondence Analysis, taking as the population the set of individuals.