Electronic Journal of Statistics

The function-on-scalar LASSO with applications to longitudinal GWAS

Rina Foygel Barber, Matthew Reimherr, and Thomas Schill

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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.

Article information

Electron. J. Statist., Volume 11, Number 1 (2017), 1351-1389.

Received: April 2016
First available in Project Euclid: 19 April 2017

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Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression 62G20: Asymptotic properties
Secondary: 62J07: Ridge regression; shrinkage estimators

Functional data analysis high-dimensional regression variable selection functional regression

Creative Commons Attribution 4.0 International License.


Barber, Rina Foygel; Reimherr, Matthew; Schill, Thomas. The function-on-scalar LASSO with applications to longitudinal GWAS. Electron. J. Statist. 11 (2017), no. 1, 1351--1389. doi:10.1214/17-EJS1260. https://projecteuclid.org/euclid.ejs/1492567399

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