Self-modelling warping functions
The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations of "q" common components, which are estimated from the data (hence the name 'self-modelling'). Even small values of "q" provide remarkable model flexibility, comparable with nonparametric methods. At the same time, this approach avoids overfitting because the common components are estimated combining data across individuals. As a convenient by-product, component scores are often interpretable and can be used for statistical inference (an example of classification based on scores is given). Copyright 2004 Royal Statistical Society.
Year of publication: |
2004
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Authors: | Gervini, Daniel ; Gasser, Theo |
Published in: |
Journal of the Royal Statistical Society Series B. - Royal Statistical Society - RSS, ISSN 1369-7412. - Vol. 66.2004, 4, p. 959-971
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Publisher: |
Royal Statistical Society - RSS |
Saved in:
freely available
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