Nonparametric independence testing via mutual information

Nonparametric independence testing via mutual information. Berrett, T B & Samworth, R J Biometrika, 106(3):547–566, September, 2019.

Paper doi abstract bibtex

We propose a test of independence of two multivariate random vectors, given a sample from the underlying population. Our approach is based on the estimation of mutual information, whose decomposition into joint and marginal entropies facilitates the use of recently developed efficient entropy estimators derived from nearest neighbour distances. The proposed critical values may be obtained by simulation in the case where an approximation to one marginal is available or by permuting the data otherwise. This facilitates size guarantees, and we provide local power analyses, uniformly over classes of densities whose mutual information satisfies a lower bound. Our ideas may be extended to provide new goodness-of-fit tests for normal linear models based on assessing the independence of our vector of covariates and an appropriately defined notion of an error vector. The theory is supported by numerical studies on both simulated and real data.

@article{berrett_nonparametric_2019,
	title = {Nonparametric independence testing via mutual information},
	volume = {106},
	issn = {0006-3444},
	url = {https://doi.org/10.1093/biomet/asz024},
	doi = {10.1093/biomet/asz024},
	abstract = {We propose a test of independence of two multivariate random vectors, given a sample from the underlying population. Our approach is based on the estimation of mutual information, whose decomposition into joint and marginal entropies facilitates the use of recently developed efficient entropy estimators derived from nearest neighbour distances. The proposed critical values may be obtained by simulation in the case where an approximation to one marginal is available or by permuting the data otherwise. This facilitates size guarantees, and we provide local power analyses, uniformly over classes of densities whose mutual information satisfies a lower bound. Our ideas may be extended to provide new goodness-of-fit tests for normal linear models based on assessing the independence of our vector of covariates and an appropriately defined notion of an error vector. The theory is supported by numerical studies on both simulated and real data.},
	number = {3},
	urldate = {2023-03-24},
	journal = {Biometrika},
	author = {Berrett, T B and Samworth, R J},
	month = sep,
	year = {2019},
	pages = {547--566},
	file = {Berrett and Samworth - 2019 - Nonparametric independence testing via mutual info.pdf:/Users/soumikp/Zotero/storage/35I52TTT/Berrett and Samworth - 2019 - Nonparametric independence testing via mutual info.pdf:application/pdf;Full Text PDF:/Users/soumikp/Zotero/storage/MRDSUINH/Berrett and Samworth - 2019 - Nonparametric independence testing via mutual info.pdf:application/pdf;Full Text PDF:/Users/soumikp/Zotero/storage/54UWYR5A/Berrett and Samworth - 2019 - Nonparametric independence testing via mutual info.pdf:application/pdf;pnas.1715593115.sapp.pdf:/Users/soumikp/Zotero/storage/GEM55VPC/pnas.1715593115.sapp.pdf:application/pdf;Snapshot:/Users/soumikp/Zotero/storage/G7I6Y6FV/5511208.html:text/html},
}

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