Open Access
Translator Disclaimer
2017 The function-on-scalar LASSO with applications to longitudinal GWAS
Rina Foygel Barber, Matthew Reimherr, Thomas Schill
Electron. J. Statist. 11(1): 1351-1389 (2017). DOI: 10.1214/17-EJS1260


We present a new methodology for simultaneous variable selection and parameter estimation in function-on-scalar regression with an ultra-high dimensional predictor vector. We extend the LASSO to functional data in both the dense functional setting and the sparse functional setting. We provide theoretical guarantees which allow for an exponential number of predictor variables. Simulations are carried out which illustrate the methodology and compare the sparse/functional methods. Using the Framingham Heart Study, we demonstrate how our tools can be used in genome-wide association studies, finding a number of genetic mutations which affect blood pressure and are therefore important for cardiovascular health.


Download Citation

Rina Foygel Barber. Matthew Reimherr. Thomas Schill. "The function-on-scalar LASSO with applications to longitudinal GWAS." Electron. J. Statist. 11 (1) 1351 - 1389, 2017.


Received: 1 April 2016; Published: 2017
First available in Project Euclid: 19 April 2017

zbMATH: 1362.62084
MathSciNet: MR3635916
Digital Object Identifier: 10.1214/17-EJS1260

Primary: 62G08 , 62G20
Secondary: 62J07

Keywords: Functional data analysis , functional regression , high-dimensional regression , Variable selection


Vol.11 • No. 1 • 2017
Back to Top