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Title: Functional data and nonparametric modelling: Theoretical/methodological/practical aspects Authors:  Frederic Ferraty - Mathematics Institute of Toulouse (France) [presenting]
Abstract: Situations when one observes a response (scalar or functional variable) and functional predictor(s) are considered. The natural statistical question is very simple: are we able to predict correctly the response from the functional predictor(s) when one has no idea on the relationship between the response and functional predictor(s)? A suitable answer to this important statistical issue is the ``functional nonparametric regression''. The word ``nonparametric'' stands for any model requiring very few assumptions with respect to the relationship between the response and the predictor(s); the word ``functional'' reminds that the model has to handle functional data. So, the aim is to give an extensive overview on this statistical topic. In addition to some theoretical and practical key developments, real datasets illustrate the purpose (benchmark datasets, hyperspectral image, forensic entomology in the context of criminology, etc).