CIRM, Luminy, Marseille
Date(s) : 08/02/2016 - 12/02/2016 iCal
0 h 00 min
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:
Quantum statistical models and inference.
Inverse problems in econometrics: examples and specific theoretical problems.
Geometry and inverse problems. Example tomography and astrophysics.
Oleg Lepski (Aix-Marseille Université)
Florent Autin (Aix-Marseille Université)
Quantum statistical models and inference
Multiplier bootstrap for change point detection
Convex programming approach to robust estimation of a multivariate
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
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
Aggregation of regularized rankers by means of a linear functional strategy
From prediction error to estimation error bounds
Sharp minimax and adaptive variable selection