A General Framework for the Analysis of Kernel-based Tests

Kernel-based tests provide a simple yet effective framework that uses the theory of reproducing kernel Hilbert spaces to design non-parametric testing procedures. In this paper, we propose new theoretical tools that can be used to study the asymptotic behaviour of kernel-based tests in various data scenarios and in different testing problems. Unlike current approaches, our methods avoid working with U and V-statistics expansions that usually lead to lengthy and tedious computations and asymptotic approximations. Instead, we work directly with random …

analysis data design framework general kernel non-parametric paper parametric simple spaces study testing tests theory tools