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Activity Number: 471 - New Frontier in Developments of Complex-Structured High-Dimensional Data Analysis
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #320824
Title: Functional Group Lasso with Functional Predictor Selection
Author(s): Jun Song* and Ali Mahzarnia
Companies: Korea University and UNC Charlotte
Keywords: functional group lasso; sparse estimation; covariate selection
Abstract:

In this talk, we will discuss functional predictor selection along with the estimation of smooth functional coefficients in a simultaneous fashion in a scalar-on-function regression problem under a high-dimensional multivariate functional data setting. In particular, we develop a method for functional group-sparse regression under a generic Hilbert space of infinite dimension. Each group is based on an infinite-dimensional space. Then we show the convergence of algorithms and the consistency of the estimation and selection under infinite-dimensional Hilbert spaces. Simulation and fMRI data application will be presented at the end to show the effectiveness of the methods in both the selection and estimation of functional coefficients.


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