STATISTICS COLLOQUIUM
Néhémy Lim
Visiting Assistant Professor
Department of Statistics
University of Connecticut
Balancing Statistical and Computational Precision for Efficient Variable Selection
Abstract
Driven by the advances in technology, large and high-dimensional data have become the rule rather than the exception. Approaches that allow for variable selection with such data are thus highly sought after, in particular, since standard methods, like cross-validated Lasso, can be computationally intractable and, in any case, lack theoretical guarantees. In this paper, we propose a novel approach to variable selection in regression. Consisting of simple optimization steps and tests, it is computationally more efficient than existing methods and, therefore, suited even for very large data sets. Moreover, in contrast to standard methods, it is equipped with sharp statistical and computational guarantees. We thus expect that our algorithm can help to leverage the increasing volume of data in Biology, Public Health, Astronomy, Economics, and other fields.
DATE: Wednesday, October 25, 2017
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu