Seminar Details

Seminar Details


Apr 18

3:30 pm

Smooth James-Stein Model Selection in Wavelet Smoothing, Parametric Linear Model and Inverse Problem

Sylvain Sardy


Université de Genève - Department of Mathematics

Motivated by a gravitational wave burst detection problem from several detectors, we derive smooth James-Stein (SJS) thresholding-based estimators in three settings: nonparametric and parametric regression, and inverse problem. SJS estimators enjoy smoothness like ridge regression and perform variable selection like lasso. They have added flexibility thanks to more than one regularization parameters, and the ability to select these parameters well thanks to an unbiased and smooth estimation of the risk.