Hierarchical adaptive regression kernels for regression with functional predictors

Dawn B. Woodard, Ciprian Crainiceanu, David Ruppert

Research output: Contribution to journalArticle

Abstract

We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in the online supplementary materials.

Original languageEnglish (US)
Pages (from-to)777-800
Number of pages24
JournalJournal of Computational and Graphical Statistics
Volume22
Issue number4
DOIs
StatePublished - Jan 1 2013

Keywords

  • Electroencephalogram
  • Functional data analysis
  • Functional linear model
  • Kernel mixture
  • Lévy adaptive regression kernels
  • Nonparametric Bayes

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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