PCA-based discrimination of partially observed functional data, with an application to AneuRisk65 data set

Functional data are usually assumed to be observed on a common domain. However, it is often the case that some portion of the functional data is missing for some statistical unit, invalidating most of the existing techniques for functional data analysis. The development of methods able to handle partially observed or incomplete functional data is currently attracting increasing interest. We here briefly review this literature. We then focus on discrimination based on principal component analysis and illustrate a few possible methods via simulation studies and an application to the AneuRisk65 data set. We show that carrying out the analysis over the full domain, where at least one of the functional data is observed, may not be the optimal choice for classification purposes.