M28. Regularization Methods and Applications in Statistics and Econometrics

Ill-posed inverse problems arise in many applications of statistics and econometrics. Typical examples are the estimation of a density function (with the deconvolution problem) or of a regression function (with the nonparametric instrumental regression). One main difference with standard cases is that the operator characterizing the inverse problem is a statistical object that is often unknown and must be estimated.

The objective of this mini-symposium is to gather experts and young researches to discuss about recent advances in regularization methods and applications in statistical and econometrics issues.

Organizers:
Pierre Maréchal, University of Toulouse, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
Anne Vanhems, Toulouse Business School, France, This email address is being protected from spambots. You need JavaScript enabled to view it.

Speakers (in alphabetical order):
Jean-Pierre Florens, Toulouse School of Economics, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
Nonidentified Inverse Problems with Econometric Examples

Fredrik Hildrum, Norwegian University of Science and Technology, Norway, This email address is being protected from spambots. You need JavaScript enabled to view it.
Total variation-based Lavrentiev regularization in Volterra equations of the first kind

Jan Johannes, University of Heidelberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Adaptive minimax testing in inverse problems with unknown operator

Clément Marteau, University of Lyon 1, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
Sparse Regularization for Mixture Problems

Walter Simo Tao Lee, University of Toulouse, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
Mollifying in nonparametric instrumental regression setting

Benjamin Sprung, Institute for Numerical and Applied Mathematics, Gottingen, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Convergence rates for variational regularization of statistical inverse problems

Anne Vanhems, Toulouse Business School, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
A mollifier approach to the deconvolution of probability densities