On the application of independence tests to variable selection problems

Abstract

We explore the potential of kernel-based generalizations of distance covariance for variable screening procedures. The flexibility of this association measure allows for the integration of models with spherical and hyperspherical data, which are commonly encountered in fields such as meteorology, geology, biology, and more. Through extensive empirical studies, we demonstrate the robustness and adaptability of our proposed method.