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Methods for scalar-on-function regression
International Statistical Review (2017)
  • Philip T. Reiss
  • Jeff Goldsmith, Columbia University
  • Han Lin Shang, Australian National University
  • R. Todd Ogden, Columbia University
Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. 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, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.
  • functional additive model,
  • functional generalized linear model,
  • functional linear model,
  • functional polynomial regression,
  • functional single-index model,
  • nonparametric functional regression
Publication Date
August, 2017
Citation Information
Philip T. Reiss, Jeff Goldsmith, Han Lin Shang and R. Todd Ogden. "Methods for scalar-on-function regression" International Statistical Review Vol. 85 Iss. 2 (2017) p. 228 - 249
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