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Mathematics

D-Index
34
Citations
26239
World Ranking
2835
National Ranking
169

Research.com Recognitions

  • 1998 - COPSS Presidents' Award

Overview

Pascal Massart is a researcher affiliated with the University of Paris-Saclay in France. Their work spans multiple fields of study, primarily focusing on Engineering and Mathematics.

The scientist has contributed to several subfields including Automotive Engineering, Statistics and Probability, Electrical and Electronic Engineering, Signal Processing, and Numerical Analysis.

Key research topics associated with Pascal Massart include:

  • Statistical Methods and Inference
  • Electric Vehicles and Infrastructure
  • Transportation and Mobility Innovations
  • Advanced Battery Technologies Research
  • Time Series Analysis and Forecasting
  • Mathematical Approximation and Integration
  • Gaussian Processes and Bayesian Inference

Their publication record features papers in various venues, with frequent contributions found in:

  • arXiv (Cornell University)
  • Energies
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • ESAIM Probability and Statistics

Recent significant papers by Pascal Massart and collaborators include:

  • "A Review of Electric Vehicle Load Open Data and Models", 2021, published in Energies
  • "Random Forests for Time Series", 2022, published in HAL (Le Centre pour la Communication Scientifique Directe)
  • "Numerical performance of penalized comparison to overfitting for multivariate kernel density estimation", 2022, published in ESAIM Probability and Statistics
  • "Concentration inequalities and cut-off phenomena for penalized model selection within a basic Rademacher framework", 2025, published in arXiv (Cornell University)
  • "Is model selection possible for the ℓ_p-loss? PCO estimation for regression models", 2025, published in arXiv (Cornell University)

Pascal Massart has collaborated frequently with a core group of coauthors, including:

  • Vincent Rivoirard
  • Jean-Michel Poggi
  • Claire Lacour
  • Yvenn Amara-Ouali
  • Yannig Goude

Their work has been recognized with awards such as the COPSS Presidents' Award in 1998.

Best Publications

  • Least angle regression

    Bradley Efron;Trevor Hastie;Iain Johnstone;Robert Tibshirani

  • Concentration Inequalities: A Nonasymptotic Theory of Independence

    Stéphane Boucheron;Gábor Lugosi;Pascal Massart

  • Adaptive estimation of a quadratic functional by model selection

    B. Laurent;P. Massart

  • Concentration Inequalities and Model Selection

    Pascal Massart;Jean Picard;École d'été de probabilités de Saint-Flour

  • The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality

    P. Massart

  • Risk bounds for model selection via penalization

    Andrew Barron;Lucien Birgé;Pascal Massart

  • Gaussian model selection

    Lucien Birgé;Pascal Massart

  • Minimum contrast estimators on sieves: exponential bounds and rates of convergence

    Lucien Birgé;Pascal Massart

  • From Model Selection to Adaptive Estimation

    Lucien Birgé;Pascal Massart

  • Minimal Penalties for Gaussian Model Selection

    Lucien Birgé;Pascal Massart

  • About the constants in Talagrand's concentration inequalities for empirical processes

    Pascal Massart

  • Risk bounds for statistical learning

    Pascal Massart;Élodie Nédélec

  • Some applications of concentration inequalities to statistics

    Pascal Massart

  • Invariance principles for absolutely regular empirical processes

    P. Doukhan;P. Massart;E. Rio

  • Rates of convergence for minimum contrast estimators

    Lucien Birgé;Pascal Massart

  • Concentration inequalities using the entropy method

    Stéphane Boucheron;Gábor Lugosi;Pascal Massart

  • Estimation of Integral Functionals of a Density

    Lucien Birge;Pascal Massart

  • A sharp concentration inequality with application

    Stéphane Boucheron;Gábor Lugosi;Pascal Massart

  • The functional central limit theorem for strongly mixing processes

    Paul Doukhan;Pascal Massart;Emmanuel Rio

  • Statistical Performance of Support Vector Machines

    Gilles Blanchard;Olivier Bousquet;Pascal Massart

  • Concentration inequalities : a non asymptotic theory of independence

    Stéphane Boucheron;Gabor Lugosi;Gabor Lugosi;Pascal Massart

Frequent Co-Authors

Gábor Lugosi
Gábor Lugosi Pompeu Fabra University
Saharon Rosset
Saharon Rosset Tel Aviv University
Olivier Bousquet
Olivier Bousquet Google (United States)
Frédéric Chazal
Frédéric Chazal French Institute for Research in Computer Science and Automation - INRIA
Sanford Weisberg
Sanford Weisberg University of Minnesota
Bradley Efron
Bradley Efron Stanford University
Robert Tibshirani
Robert Tibshirani Stanford University
David Madigan
David Madigan Northeastern University
Trevor Hastie
Trevor Hastie Stanford University
Iain M. Johnstone
Iain M. Johnstone Stanford University

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