On prediction rate in partial functional linear regression Academic Article uri icon


MeSH Major

  • Light
  • Microscopy
  • Optical Tweezers


  • We consider a prediction of a scalar variable based on both a function-valued variable and a finite number of real-valued variables. For the estimation of the regression parameters, which include the infinite dimensional function as well as the slope parameters for the real-valued variables, it is inevitable to impose some kind of regularization. We consider two different approaches, which are shown to achieve the same convergence rate of the mean squared prediction error under respective assumptions. One is based on functional principal components regression (FPCR) and the alternative is functional ridge regression (FRR) based on Tikhonov regularization. Also, numerical studies are carried out for a simulation data and a real data. © 2011 Elsevier Inc.

publication date

  • January 2012



  • Academic Article


Digital Object Identifier (DOI)

  • 10.1016/j.jmva.2011.06.011

Additional Document Info

start page

  • 93

end page

  • 106


  • 103


  • 1