Self‐modelling warping functions

Daniel Gervini and Theo Gasser

Journal of the Royal Statistical Society Series B, 2004, vol. 66, issue 4, 959-971

Abstract: Summary. 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).

Date: 2004
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