Abstract
The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations ofqcommon components, which are estimated from the data (hence the name‘self-modelling’). Even small values ofqprovide 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). [ABSTRACT FROM AUTHOR]
Gervini, D