Multivariate nonparametric test of independence

By Yanan Fan, Pierre Lafaye de Micheaux, Spiridon I. Penev, Donna Salopek. 2017

74 citations recorded by the source corpus.

The problem of testing mutual independence of p random vectors in a general setting where the dimensions of the vectors can be different and the distributions can be discrete, continuous or both is of great importance. We propose such a test which utilizes multivariate characteristic functions and is a generalization of known results. We characterize the limiting distribution of the test statistic under the null hypothesis. The limiting null distribution is approximated and the method is validated. Numerical results based on simulations are investigated and our methodology is implemented in the R package IndependenceTests. Power comparisons are also presented for some partial cases of our general test, where some competitive procedures exist.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

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Citations in the corpus

Nonparametric independence testing via mutual information (2017, background): “This concept has also been studied in high dimensions (Sz´ekely and Rizzo, 2013; Yao, Zhang and Shao, 2017), and for testing independence of several random vectors (Fan et al., 2017).”
Time-Varying Periodicity in Intraday Volatility (2019, methodology): “Applications of this approach for testing dependence between two variables can be found in, for example, Bakirov, Rizzo, and Székely (2006, 2007), Csörgö (1985), Fan et al. (2017), and Rizzo and Székely (2009).”