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Activity Number: 416 - SLDS CSpeed 7
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318040
Title: EAS Methodology for Grouped Variable Selection in Multivariate Linear Model
Author(s): Salil Koner* and Jonathan P Williams
Companies: North Carolina State University and North Carolina State University
Keywords: Epsilon admissible subsets; Matrix-Normal; Generalized fiducial distribution; Variable selection; High-dimensional linear regression; Asymptotically consistent
Abstract:

In this paper, we extend the epsilon admissible subsets (EAS) model selection approach, from its original construction in the high-dimensional linear regression setting, to an EAS framework for performing group variable selection in the high-dimensional multivariate regression setting. Assuming a matrix-Normal linear model we show that the EAS strategy is asymptotically consistent if there exists a spare, true data generating set of predictors. Nonetheless, our EAS strategy is designed to estimate a posterior-like, generalized fiducial distribution over a parsimonious class of models in the setting of correlated predictors and/or in the absence of a sparsity assumption. The effectiveness of our approach, to this end, is demonstrated empirically in simulation studies, and is compared to other state-of-the-art model/variable selection procedures. Furthermore, our framework has been designed with particular consideration for applying the method to discretized functional data. Special care is needed for extending our EAS strategy to the functional data setting, but we demonstrate how this can be done with numerical illustrations.


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

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