We informally call a stochastic process learnable if it admits a generalization error
approaching zero in probability for any concept class with finite VC-dimension (IID …

We give an introduction to a notion of weak dependence which is more general than mixing
and allows to treat for example processes driven by discrete innovations as they appear with …

For a stationary random field $(X_j) _ {j\in\mathbb {Z}^ d} $ and some measure $\mu $ on
$\mathbb {R}^ d $, we consider the set-indexed weighted sum process\[S_n (A)=\sum …

The paper is devoted to recall weak dependence conditions from Dedecker et al.(Weak
dependence, examples and applications. Lecture Notes in Statistics, vol 190, 2007)'s …

Ratios of random variables often appear in probability and statistical applications. We aim to
approximate the moments of such ratios under several dependence assumptions. Extending …

Abstract Let (T, C, X) be a vector of random variables (rvs) where T, C, and X are the interest
variable, a right censoring rv, and a covariate, respectively. In this paper, we study the kernel …

Assume that (Xn) n∈ Z is a sequence of Rd valued random variables with common
distribution which is absolutely continuous with respect to Lebesgue's measure, with density …

Taylor's power law (or fluctuation scaling) states that on comparable populations, the
variance of each sample is approximately proportional to a power of the mean of the …

This paper provides extensions of the work on subsampling by Bertail et al. in J. Econ. 120:
295–326 (2004) for strongly mixing case to weakly dependent case by application of the …

Self-normalized central limit theorems are important for statistical purposes. A simple way to
achieve them is to consider estimations of the limit variance; this expression writes as a …