In the recent past years, many scientific fields in which applied statistics is involved are recording continuous/functional data. Basically, the scope of disciplines for which such functional data (or curves data) may appear is covering diverse areas such as medicine, econometrics, environmetrics, biostatistics, geophysics, chemometrics, etc. It is therefore a real challenge for the statistical community to develop new tools able to deal with such a functional data context. This challenge has both theoretical and applied implications.

This track aims to promote the developments of new statistical advances for functional data, covering foundations, methodology and applications. The following topics will be of particular interest:

• linear and generalized linear models for functional data;
• nonparametric modelling for functional variables;
• bootstrapping functional data;
• learning with functional data (boosting, bagging, neural networks, etc.);
• classification/discrimination of functional data;
• testing hypothesis and model selection;