Abstract
In this chapter we introduce a third option for approximating discrete data by a function. The roughness penalty or regularization approach retains the advantages of the basis function and local expansion smoothing techniques developed in Chapter 3, but circumvents some of their limitations. More importantly, it adapts gracefully to more general functional data analysis problems that we consider in subsequent chapters.
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© 1997 Springer Science+Business Media New York
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Ramsay, J.O., Silverman, B.W. (1997). The roughness penalty approach. In: Functional Data Analysis. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-7107-7_4
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DOI: https://doi.org/10.1007/978-1-4757-7107-7_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-7109-1
Online ISBN: 978-1-4757-7107-7
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