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Recherche

Enseignements

Conférences
  et invitations 

Brief CV

 

Professeur à l'Université Paris-Est Marne-la-Vallée

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      https://perso.math.u-pem.fr/butucea.cristina/CB.JPG

 

Laboratoire d'Analyse et de Mathématiques Appliquées

 

UMR CNRS 8050 - UFR Mathématiques

 

Université Paris-Est Marne-la-Vallée

 

5, bd Descartes, Champs sur Marne

 

77454 Marne-la-Vallée Cedex 2

 

 

2019 IMS Fellow

 

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Conference organisation:

Mathematical Foundations of Machine Learning, (with P. Bartlett, J. Schmidt-Hieber)  MFOberwolfach, March 21-27, 2021

Mathematical Methods of Statistics, (with S. Minsker, C. Pouet, V. Spokoiny) Luminy, December 14-18, 2020

Mathematical Methods of Statistics, (with C. Pouet, V. Spokoiny) Luminy, December 15-20, 2019

Mathematical Methods of Statistics, (with C. Pouet, V. Spokoiny) Fréjus, December 16-21, 2018

Workshop on High-Dimensional problems and quantum physics, Champs-sur-Marne, Marne-la-Vallée, 8-10 June 2015

Workshop Statistique des nouvelles données, Champ-sur-Marne, Marne-la-Vallée, June 2012

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Associate Editor of ALEA, Latin American Journal of Probability and Mathematical Statistics, 2016-, (ALEA)

Associate Editor of the Electronic Journal of Statistics, 2010-2012, (EJS)

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Thèmes de recherche

Statistique non paramétrique. 

Estimation minimax et adaptative sur des classes de régularité; vitesses, optimalité au sens minimax et efficacité; tests minimax et adaptatifs.

    Modèle de densité avec observations i.i.d ou mélangeantes;
    Modèles à erreurs sur les variables (densité et régression) non et semi-paramétriques;
    Tests d'adéquation et d'homogénéité;
    Problèmes inverses;
    Statistique quantique.

Estimateurs à noyau, ondelettes, M-estimateurs.
 

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Research

Nonparametric statistics. 

Minimax and adaptive estimation over smoothness classes;  rates, optimality in the minimax sense and sharp results; minimax and adaptive tests.

    Density model with i.i.d. and mixing data;
    Nonparametric and semi-parametric errors-in-variables models (density and regression models);
    Goodness-of-fit and homogeneity tests;
    Inverse problems;
   Quantum statistics.

Kernel smoothing, wavelets, M-estimators.