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

Rina Foygel Barber, Matthew Reimherr, Thomas Schill

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1351-1389
Number of pages39
JournalElectronic Journal of Statistics
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2017

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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