Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

Heuristically, a discrete-time stochastic process has the Markov property if the past and
future are independent given the present. In this introductory chapter, we give the formal …

We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the
test determines how likely it is that these were generated from a target density function. The …

This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …

Dariusz Buraczewski Ewa Damek Thomas Mikosch The Equation X = AX + B Page 1
Springer Series in Operations Research and Financial Engineering Dariusz Buraczewski …

We propose a robust method for constructing conditionally valid prediction intervals based
on models for conditional distributions such as quantile and distribution regression. Our …

These lecture notes are the second version of the book “Théorie asymptotique des
processus aléatoires faiblement dépendants”, written in French. In the process of translation …

This book is dedicated to the mathematical study of two-dimensional statistical
hydrodynamics and turbulence, described by the 2D Navier–Stokes system with a random …

We review some regression models for the analysis of count time series. These models have
been the focus of several investigations over the last years, but only recently simple …

In this paper, we perform deep neural networks for learning stationary ψ-weakly dependent
processes. Such weak-dependence property includes a class of weak dependence …