Activity Number:
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361
- Contributed Poster Presentations: Section on Nonparametric Statistics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #314072
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Title:
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Simultaneous Estimation and Variable Selection for Functional Regression Model via Rank-Based Regularization
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Author(s):
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Jieun Park* and Ash Abebe and Nedret Billor
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Companies:
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Auburn University at Montgomery and Auburn University and Auburn University
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Keywords:
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Robust;
Rank-based;
Regularization;
Variable selection;
Functional analysis
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Abstract:
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We propose a robust rank based variable selection method for a functional linear regression model with multiple explanatory functions and a scalar response. The procedure extends rank based group variable selection to functional variable selection and the proposed estimator is robust in the presence of outliers in predictor function space as well as response space. The performance of the proposed robust method is demonstrated with an extensive simulation study and real data examples. We prove the proposed method with a group-adaptive penalty achieves the oracle property.
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Authors who are presenting talks have a * after their name.