Kernel-based Partial Permutation Test for Detecting Heterogeneous Functional Relationship

TL;DR

This paper introduces a kernel-based partial permutation test for assessing the equality of functional relationships across groups, offering exact finite-sample validity for certain models and asymptotic validity for broader classes.

Contribution

It develops a flexible, kernel-based permutation testing framework with exact and asymptotic validity, including efficient algorithms and extensions to multiple kernels and non-Gaussian noise.

Findings

Exact finite-sample validity for linear and polynomial kernels.

Asymptotic validity for general kernels with diverging feature spaces.

Effective algorithms using EM and Newton's methods.

Abstract

We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector $\boldsymbol{Y}$ on leading principle components of a kernel matrix fixed and permute $\boldsymbol{Y}$'s projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of functions under the null hypothesis. First, using linear or polynomial kernels, our partial permutation tests are exactly valid in finite samples for linear or polynomial regression models with Gaussian noise; similar results straightforwardly extend to kernels with finite feature spaces. Second, by allowing the kernel feature space to diverge with the sample size, the test can be large-sample valid for a wider class of functions. Third, for general kernels with possibly infinite-dimensional feature space, the partial permutation test is exactly valid when the covariates are exactly balanced across all groups, or asymptotically valid when the underlying function follows certain regularized Gaussian processes. We further suggest test statistics using likelihood ratio between two (nested) GPR models, and propose computationally efficient algorithms utilizing the EM algorithm and Newton's method, where the latter also involves Fisher scoring and quadratic programming and is particularly useful when EM suffers from slow convergence. Extensions to correlated and non-Gaussian noises have also been investigated theoretically or numerically. Furthermore, the test can be extended to use multiple kernels together and can thus enjoy properties from each kernel. Both simulation study and application illustrate the properties of the proposed test.