Event Date
Speaker: Arian Maleki (Associate Professor, Columbia University)
Title: Comparing Variable Selection Techniques Under a High-Dimensional Asymptotic
Abstract: In this talk, we discuss the problem of variable selection for linear models under the high-dimensional asymptotic setting, where the number of observations, n, grows at the same rate as the number of predictors, p. We consider two-stage variable selection techniques (TVS) in which the first stage obtains an estimate of the regression coefficients, and the second stage simply thresholds this estimate to select the “important” predictors. The asymptotic false discovery proportion (AFDP) and true positive proportion (ATPP) of these TVS are evaluated, and their optimality will be discussed.
About the speaker: Arian Maleki is currently an Associate Professor of Statistics at Columbia University. His research interest includes compressed sensing, high-dimensional statistics, and machine learning.
Seminar Date/Time: Thursday February 25, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Xiucai Ding (xcading@ucdavis.edu) or Pete Scully (pscully@ucdavis.edu) for the meeting ID and password, stating your affiliation.