A kernel-based framework for covariate significance tests in nonparametric regression

Abstract: It is well known that nonparametric regression estimation and inference procedures are subject to the curse of dimensionality. Moreover, model interpretability usually decreases with the data dimension. Therefore, model-free variable selection procedures and, in particular, covariate significance tests, are invaluable tools for regression modelling as they help to remove irrelevant covariates. In this contribution, we provide a general framework, based on recent developments in the theory of kernel-based characterizations of null conditional expectations, for testing the significance of a subgroup of Hilbert space-valued covariates in a nonparametric regression model. Moreover, we propose a test designed to be robust against the curse of dimensionality and we provide some asymptotic results regarding the distribution of the test statistic under the null hypothesis of non-significant covariates as well as under fixed and local alternatives. Regarding the test calibration, we present and prove the consistency of a multiplier bootstrap scheme. An extensive simulation study is conducted to assess the finite sample performance of the test. We also apply our test in a real data scenario