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A note on testing complete independence for high dimensional data

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  • Mao, Guangyu

Abstract

The high dimensional independence test in Schott (2005) assumes (m,n)→∞ and mn→γ∈(0,∞), where m signifies the dimension and n denotes the sample size. This paper notes that without the restriction mn→γ, the test is still effective provided that (m,n)→∞, or n is fixed but m→∞.

Suggested Citation

  • Mao, Guangyu, 2015. "A note on testing complete independence for high dimensional data," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 82-85.
    • Handle: RePEc:eee:stapro:v:106:y:2015:i:c:p:82-85
    DOI: 10.1016/j.spl.2015.07.001
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    References listed on IDEAS

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    1. Mao, Guangyu, 2014. "A new test of independence for high-dimensional data," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 14-18.
    2. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
    3. James R. Schott, 2005. "Testing for complete independence in high dimensions," Biometrika, Biometrika Trust, vol. 92(4), pages 951-956, December.
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    Cited by:

    1. He, Daojiang & Liu, Huanyu & Xu, Kai & Cao, Mingxiang, 2021. "Generalized Schott type tests for complete independence in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    2. Liqi Xia & Ruiyuan Cao & Jiang Du & Jun Dai, 2025. "Consistent complete independence test in high dimensions based on Chatterjee correlation coefficient," Statistical Papers, Springer, vol. 66(1), pages 1-32, January.

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