Week 2: Mathematical Statistics and Inverse Problems
February 8 – 12, 2016
The main goal in this week is to bring together leading researchers in the area of mathematical statistics and inverse problems in order to exchange the ideas and initialize new researches.
The workshop will be focusing on the following topics: regularization of ill-posed inverse problems, density deconvolution, quantum statistics, multivariate structural functional estimation and detection} along with applications related to econometrics, tomography and astrophysics.

During the workshop three  mini-courses will be given:

  • Cristina Butucea (Université Paris Est-Marne la Vallée

Quantum statistical models and inference.

  • Jean-Pierre Florens (Université Toulouse1) 

Inverse problems in econometrics: examples and specific theoretical problems.

  • Gerard Kerkyacharian (Université Pierre et Marie Curie)

Geometry and inverse problems. Example tomography and astrophysics.

Scientific Committee

Oleg Lepski (Aix-Marseille Université)
Dominique Picard (Université Paris Diderot)

Organizing Committee

Florent Autin (Aix-Marseille Université)
Yuri Golubev (Aix-Marseille Université)
Christophe Pouet (Ecole Centrale Marseille)


Quantum statistical models and inference

Multiplier bootstrap for change point detection

Convex programming approach to robust estimation of a multivariate
Gaussian model

Bump detection in a heterogeneous Gaussian regression

On consistent hypothesis testing

Inverse problems in econometrics: examples and specific theoretical prob-

Minimax optimal detection of structure for multivariate data

The M/G/infinit estimation problem revisited

Denoising nonlinear dynamical systems

Variational Regularization of Nonlinear Statistical Inverse Problems

Drift estimation in sparse sequential dynamic imaging

Estimation of in nite-dimensional parameter in lp spaces

  • Jan Johannes (CREST-Ensai and Université catholique de Louvain)

Adaptive Bayesian estimation in indirect Gaussian sequence space models

Geometry and inverse problems. Example tomography and astrophysics

Adaptive Estimation in the Convolution Structure Density Model

Minimax goodness-of- t testing in ill-posed inverse problems with partially unknown operators

Discrepancy based model selection in statistical inverse problems

Nonparametric admissible estimator

Statistical Blind Source Separation

Laplace deconvolution and its application to the analysis of dynamic
contrast enhanced imaging data

Aggregation of regularized rankers by means of a linear functional strategy

From prediction error to estimation error bounds

Sharp minimax and adaptive variable selection