ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2022
The Chi-Square Test of Distance Correlation.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models.The British journal of mathematical and statistical psychology · 2025Article
- Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI.Magma (New York, N.Y.) · 2025Article
- An autonomous vehicles' test case extraction method: Example of vehicle-to-pedestrian scenarios.Heliyon · 2025Article
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- Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models.PloS one · 2025Article
- Multiscale comparative connectomics.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Assessing body mass index stages, individual diabetes and hypertension history effects on the risk of developing hypertension among Ghanaians: A cross-sectional study.Health science reports · 2023Article
- Mixed effects logistic regression analysis of blood pressure among Ghanaians and associated risk factors.Scientific reports · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data.Sensors (Basel, Switzerland) · 2023Article
- Significance of Nuclear Factor-Kappa B (NF-κB) and Survivin in Breast Cancer and Their Association with Radiosensitivity and Prognosis.Breast cancer (Dove Medical Press) · 2023Article
- Distinct noncoding RNAs and RNA binding proteins associated with high-risk pediatric and adult acute myeloid leukemias detected by regulatory network analysis.Cancer reports (Hoboken, N.J.) · 2022Article
- A Comparison of Functional Features of Chinese and US Mobile Apps for Pregnancy and Postnatal Care: A Systematic App Store Search and Content Analysis.Frontiers in public health · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this paper we propose a chi-square test for distance correlation. Method-wise, the chi-square test is non-parametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be utilized for K-sample and partial testing. Theory-wise, we show that the underlying chi-square distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-square test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test.
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