Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 145 - Trend Filtering and Related Regression Methods
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: IMS
Abstract #316869
Title: MARS via LASSO
Author(s): Dohyeong Ki and Billy Fang and Adityanand Guntuboyina*
Companies: and Google and UC Berkeley
Keywords: Nonparametric regression; trend filtering; bounded mixed derivative; curse of dimensionality; piecewise constant fitting
Abstract:

We propose and study a natural LASSO variant of the MARS method for regression. Our method is based on least squares estimation over a convex class of functions obtained by considering infinite dimensional linear combinations of functions in the MARS basis and putting a variation based complexity constraint. We show that this method can be computed via finite-dimensional convex optimization and that it can be viewed as a multivariate generalization of trend filtering. Under natural design assumptions, we prove that our estimator achieves a rate of convergence that depends only logarithmically on dimension and thus avoids the usual curse of dimensionality to some extent.


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

Back to the full JSM 2021 program