Statistical methodology for classification of partially observed functional data

This thesis is devoted to the development of new methodologies for the classication of partially observed functional data. Functional Data Analysis is nowadays one of the most active area of research in statistics. It deals mostly with data coming from technical machineries and digital instruments, treating data as functions. Classication of this kind of data is still an open problem and there are several available methods in the literature. Unfortunately, none of these methods is directly applicable when the data are partially observed, i.e. exhibit some missing parts. The aim of this work is to provide new insights and proposals for discrimination of functional fragments. The theory we develop is strongly supported by extensive simulations and all the methods are illustrated on a real medical dataset called Aneurisk, on which we outperform previous classication performance.