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Activity Number: 329 - New Statistical Learning and Methods in Nonparametric Statistics
Type: Invited
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Nonparametric Statistics
Abstract #314493
Title: Nonparametric Interaction Selection
Author(s): Yushen Dong and Yichao Wu*
Companies: University of Illinois at Chicago and University of Illinois at Chicago
Keywords: kernel; smoothing; interaction; variable selection
Abstract:

We consider the nonparametric two-way interaction model and propose a method to select important main effect and interaction effect terms simultaneously. Our method is based on back fitting local constant smoothing. Interaction selection is achieved by solving a constrained optimization problem to identify which main effect and interaction effect terms favor an infinity smoothing bandwidth. We establish selection consistency for the proposed method. Simulation examples and a real data example are used to illustrate its competitive finite-sample performance.


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

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