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?
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© 1997 Springer Science+Business Media New York
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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
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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
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