Clustering rates and Chung type functional laws of the iterated logarithm for empirical and quantile processes

Following the works of Berthet (1997), we first obtain exact clustering rates in the functional law of the iterated logarithm for the uniform empirical and quantile processes and for their increments. In a second time, we obtain functional Chung-type limit laws for the local empirical process for a class of target functions on the border of the Strassen set

A limited in bandwidth uniformity for the functional limit law of the increments of the empirical process

Consider the following local empirical process indexed by $K\in \mathcal{G}$, for fixed $h>0$ and $z\in \mathbb{R}^d$: G_n(K,h,z):=\sum_{i=1}^n K \Bigl(\frac{Z_i-z}{h^{1/d}}\Big) - \mathbbE \Bigl(K \Bigl(\frac{Z_i-z}{h^{1/d}}\Big)\Big), where the $Z_i$ are i.i.d. on $\mathbb{R}^d$. We provide an extension of a result of Mason (2004). Namely, under mild conditions on $\mathcal{G}$ and on the law of $Z_1$, we establish a uniform functional limit law for the collections of processes $\bigl\{G_n(\cdot,h_n,z), z\in H, h\in [h_n,\mathfrak{h}_n]\big\}$, where $H\subset \mathbb{R}^d$ is a compact set with nonempty interior and where $h_n$ and $\mathfrak{h}_n$ satisfy the Cs\"{o}rg\H{o}-R\'{e}v\'{e}sz-Stute conditions.Comment: Published in at http://dx.doi.org/10.1214/08-EJS193 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Donsker and Glivenko-Cantelli theorems for a class of processes generalizing the empirical process

International audienceWe establish a Glivenko-Cantelli and a Donsker theorem for a class of random discrete measures which generalize the empirical measure, under conditions on uniform entropy numbers that are common in empirical processes theory. Some illustrative applications in nonparametric Bayesian theory and randomly sized sampling are provided