Functional linear models for scalar responses

  • J. O. Ramsay
  • B. W. Silverman
Part of the Springer Series in Statistics book series (SSS)

Abstract

In this chapter, we consider a linear model defined by a set of functions, but where the response variable is scalar or multivariate. This contrasts with Chapter 9, where the responses and the parameters were functional, but, because of the finite and discrete covariate information, the linear transformation from the parameter space to the observation space was still specified by a design matrix Z as in the conventional multivariate general linear model.

Keywords

Smoothing Parameter Functional Regression Basis Expansion Fourier Basis Regression Diagnostics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • J. O. Ramsay
    • 1
  • B. W. Silverman
    • 2
  1. 1.Department of PsychologyMcGill UniversityMontrealCanada
  2. 2.Department of MathematicsUniversity of BristolBristolUK

Personalised recommendations