Speaker: Gery Geenens

Abstract: In this work we study some tests for the homogeneity between two independent samples of functional data. The null hypothesis of "homogeneity" here means that the latent stochastic processes which generated the two samples have the same distribution. Most instances of functional data are so complex that it seems natural to opt for nonparametric procedures in this setting. Making use of recent developments on functional depths, we adapt some Kolmogorov-Smirnov- and Cramer-von-Mises-type of criteria to the functional context. Exact p-values for the test can be obtained via permutations, or, in case of too large samples, a bootstrap algorithm is easily implemented. Some real data examples are analyzed.