Slope meets Lasso in sparse linear regression

Abstract: We will present results in sparse linear regression on two convex regularized estimators, the Lasso and the recently introduced Slope estimator, in the high-dimensional setting where the number of covariates p is larger than the number of observations n. The estimation and prediction performance of these estimators will be presented, as well as a comparative study of the assumptions on the design matrix.  https://arxiv.org/pdf/1605.08651.pdf

[Joint work with Guillaume Lecue and Alexandre B. Tsybakov]
Biography: I am an Assistant Professor of statistics at Rutgers, the State University of New Jersey. I obtained my PhD in 2016 from ENSAE ParisTech, where I was fortunate to have Alexandre Tsybakov as my PhD advisor. My research interests include aggregation of estimators, shape restricted regression, confidence sets, high-dimensional statistics and concentration inequalities.