248 – Contributed Oral Poster Presentations: Section on Statistical Computing
Robust Variable Selection in Functional Linear Models
Jasdeep Pannu
Auburn University
Nedret Billor
Auburn University
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.