A Minimax Two-Sample Test for Functional Data via Grothendieck’s Divergence
基于 Grothendieck 散度的函数型数据极小极大两样本检验
Our proposed approach addresses the challenges associated with nonparametric two-sample testing for densely measured functional data. These challenges stem from the high dimensionality of data and the nature of the observation scheme. We introduce a novel metric concept for random functions known as Grothendieck’s divergence to overcome these challenges, which satisfies the homogeneity-zero equivalence property. Our approach uses a pre-smoothing technique on densely measured…