Abstract
In this chapter we return to the problem that animated the development of principal components analysis in Chapter 6: can we use our set of N functional observations x i to define a much smaller set of m functions u j on the basis of which we can obtain efficient approximations of these observed functions?
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer Science+Business Media New York
About this chapter
Cite this chapter
Ramsay, J.O., Silverman, B.W. (1997). Principal differential analysis. In: Functional Data Analysis. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-7107-7_14
Download citation
DOI: https://doi.org/10.1007/978-1-4757-7107-7_14
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-7109-1
Online ISBN: 978-1-4757-7107-7
eBook Packages: Springer Book Archive