Methods for scalar-on-function regression

Philip T. Reiss*, Jeff Goldsmith, Han Lin Shang, R. Todd Ogden

*Corresponding author for this work

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images and so on are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorising the basic model types as linear, non‐linear and non‐parametric. We discuss publicly available software packages and illustrate some of the procedures by application to a functional magnetic resonance imaging data set.
Original languageEnglish
Pages (from-to)228-249
Number of pages22
JournalInternational Statistical Review
Volume85
Issue number2
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

Keywords

  • Functional additive model
  • functional generalised linear model
  • functional linear model
  • functional polynomial regression
  • functional single-index model
  • non-parametric functional regression

Fingerprint Dive into the research topics of 'Methods for scalar-on-function regression'. Together they form a unique fingerprint.

  • Cite this