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