A Simple Method for Testing Independence of High-Dimensional Random Vectors
DOI:
https://doi.org/10.17713/ajs.v37i1.291Abstract
A simple, data-driven and computationally efficient procedure for testing independence of high-dimensional random vectors 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 carried out to assess the performance of the procedure.
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