activity
20042026
most citedOn mean central limit theorems for stationary sequences

46 citations · 158 across the 52 of their papers we have counts for

collaborators

63 papers

math.ST2026

Choosing the penalty in nonparametric regression: short and long-range dependence

Emmanuel Caron Parte, Jérôme Dedecker, Bertrand Michel

In this work, we study the one-dimensional regression problem under random design and Gaussian errors. Our framework is very general: we make no prior assumptions about the design…

math.PR2026

Normal approximation for partial sums: general convex costs

Jérôme Dedecker, Florence Merlevède, Emmanuel Rio

We provide non-asymptotic bounds and asymptotic limits for convex transport costs between the distribution of partial sums of independent and identically distributed square integra…

math.PR2026

Concentration of the empirical measure in Wasserstein distance: bounds involving the covering dimension

Jérôme Dedecker, Aurélie Fischer, Bertrand Michel

We give concentration inequalities in Wasserstein distance for the empirical measure of a sequence of independent and identically distributed random variables with values in a Poli…

math.PR2025

Some remarks on Gordin-Lifšic's condition for martingale approximations

Jèrôme Dedecker, Florence Merlevède

In this note, we study a condition introduced by Gordin and Lif{\v s}ic in 1981 to establish the Central Limit Theorem for additive functionals of stationary Markov chains with nor…

stat.AP2025

Z-scores-based methods and their application to biological monitoring: An extended analysis of professional soccer players and cyclists athletes

Geoffroy C. B. Berthelot, Brigitte Gelein, Eric Meinadier +2

The increase in the collection of biological data allows for the individual and longitudinal monitoring of hematological or urine biomarkers. However, identifying abnormal behavior…

cs.MS2025

DEViaN-LM: An R Package for Detecting Abnormal Values in the Gaussian Linear Model

Geoffroy Berthelot, Guillaume Saulière, Jérôme Dedecker

The DEViaN-LM is a R package that allows to detect the values poorly explained by a Gaussian linear model. The procedure is based on the maximum of the absolute value of the studen…