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Testing independence between high‐dimensional random vectors using rank‐based max‐sum tests

Hongfei Wang et al.

Scandinavian Journal of Statistics2026https://doi.org/10.1111/sjos.70063article
AJG 3
Weight
0.50

What the paper says

In this paper, we address the problem of testing independence between two high‐dimensional random vectors. Our approach involves a series of max‐sum tests based on three well‐known classes of rank‐based correlations. These correlation classes encompass several popular rank measures, including Spearman's , Kendall's , Hoeffding's D, Blum‐Kiefer‐Rosenblatt's R, and Bergsma‐Dassios‐Yanagimoto's . The key advantages of our proposed tests are threefold: (1) they do not rely on specific assumptions about the distribution of random vectors, which makes them applicable across a wide range of settings; (2) they can effectively capture nonlinear dependence structures between random vectors, a critical aspect in high‐dimensional contexts; (3) they exhibit robust power performance under both sparse and dense alternatives. Notably, our proposed tests exhibit robust power across a variety of scenarios, as evidenced by extensive numerical results and an empirical application to RNA microarray data.

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https://doi.org/https://doi.org/10.1111/sjos.70063

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@article{hongfei2026,
  title        = {{Testing independence between high‐dimensional random vectors using rank‐based max‐sum tests}},
  author       = {Hongfei Wang et al.},
  journal      = {Scandinavian Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/sjos.70063},
}

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Testing independence between high‐dimensional random vectors using rank‐based max‐sum tests

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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