Abstract Details
Activity Number:
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248
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #312379
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Title:
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Robust Variable Selection for Functional Regression Models
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Author(s):
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Jasdeep Pannu*+ and Nedret Billor
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Companies:
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Auburn University and Auburn University
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Keywords:
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Functional Regression Model ;
L1 regularization ;
LAD-LASSO ;
Functional variable selection ;
groupLASSO ;
Outliers
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Abstract:
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We consider the problem of selecting functional variables using the L1 regularization in a functional linear regression model with a scalar response and functional predictors in the presence of outliers. Since the LASSO is a special case of the penalized least squares regression with L1-penalty function it suffers from the heavy-tailed errors and/or outliers in data. Recently the LAD-LASSO regression method is used to carry out robust parameter estimation and variable selection simultaneously for a multiple linear regression model. However variable selection of the functional predictor based on LASSO fails since multiple parameters exist for a functional predictor. Therefore group LASSO is used for selecting grouped variables rather than individual variables. In this study we extend the LAD-groupLASSO to a functional linear regression model with a scalar response and functional predictors. We illustrate the LAD-groupLASSO on both simulated and real data.
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