Irina Gaynanova

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic net, and many others. However, the common theoretical bounds for the predictive performance of these estimators hinge on strong, in practice unverifiable assumptions on the(More)
The TREX is a recently introduced method for performing sparse high-dimensional regression. Despite its statistical promise as an alternative to the lasso, square-root lasso, and scaled lasso, the TREX is computationally challenging in that it requires solving a non-convex optimization problem. This paper shows a remarkable result: despite the non-convexity(More)
  • 1