Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 416 - SLDS CSpeed 7
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319137
Title: Group Selection and Shrinkage with Application to Sparse Semiparametric Modeling
Author(s): Ryan Thompson* and Farshid Vahid
Companies: Monash University and Monash University
Keywords: Group subset selection; Group lasso; Structured sparsity; Variable selection; Coordinate descent
Abstract:

Sparse regression and classification estimators capable of group selection have application to an assortment of statistical problems, from multitask learning to sparse additive modeling to hierarchical selection. This work introduces a class of group-sparse estimators that combine group subset selection with group lasso or ridge shrinkage. We develop an optimization framework for fitting the nonconvex regularization surface and present finite-sample error bounds for estimation of the regression function. Our methods and analyses accommodate the general setting where groups overlap. As an application of group selection, we study sparse semiparametric modeling, a procedure that allows the effect of each predictor to be zero, linear, or nonlinear. For this task, the new estimators improve across several metrics on synthetic data compared to alternatives. Finally, we demonstrate their efficacy in modeling supermarket foot traffic and economic recessions using many predictors. All of our proposals are made available in the scalable implementation grpsel.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program