|
Activity Number:
|
314
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistical Learning and Data Science
|
|
Abstract #320170
|
View Presentation
|
|
Title:
|
Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty
|
|
Author(s):
|
Asad Haris* and Ali Shojaie and Noah Simon
|
|
Companies:
|
University of Washington and University of Washington and University of Washington
|
|
Keywords:
|
Nonparametric regression ;
Convex optimization ;
Big data ;
Penalized regression ;
Additive models ;
Convergence rates
|
|
Abstract:
|
We consider the task of fitting a nonparametric regression model using a growing set of basis functions. Like projection estimators, our proposal fits a truncated basis expansion. The novelty of our method lies in the data-driven mechanism for selection of the truncation level which is achieved by nested group penalties. We establish convergence rates comparable to smoothing splines via entropy methods and, a closed form solution for the resulting optimization problem. We explore its performance in a simulation study and on real data.
|
Authors who are presenting talks have a * after their name.
