Testing of Independency for High-Dimensional Data

Abstract

A simple, data-driven and computationally efficient procedure for testing independence of variables in high-dimension data is proposed. The procedure is based on interpretation of testing goodness-of-fit as the classification problem, a special sequential partition procedure, elements of sequential testing, resampling and randomization. Monte Carlo simulations are used to assess the performance of the procedure.