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Self-modelling warping functions

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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).
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Keywords: Curve registration; Functional data analysis; Semiparametric models; Time warping

Document Type: Research Article

Affiliations: University of Zürich, Switzerland

Publication date: November 1, 2004

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