| Université Paris 6 Pierre et Marie Curie | Université Paris 7 Denis Diderot | |
| CNRS U.M.R. 7599 | ||
| ``Probabilités et Modèles Aléatoires'' | ||
Auteur(s):
Code(s) de Classification MSC:
Résumé: We test whether two independent samples of i.i.d. random variables $X_1,\ldots,X_n$ and $Y_1,\ldots,Y_m$ having common probability density $f$ and, respectively, $g$ are issued from the same population. The null hypothesis $f=g$ is opposed to a large nonparametric class of smooth alternatives $f$ and $g$. We consider several problems, according to the distance between the populations' densities: pointwise, interval-wise, $L_2$ and $L_\infty$ norms. We propose test procedures that attain parametric rates in some cases. In other problems, the procedures adapt automatically to the smoothnesses of the underlying densities. After a numerical study of these tests, we prove their theoretical properties in the classical minimax approach.
Mots Clés: Nonparametric test ; Homogeneity test ; Wavelet estimator ;
Minimax rates ; Adaptivity
Date: 2003-12-15
Prépublication numéro: PMA-871