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Invariant correlation under marginal transforms

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  • Koike, Takaaki
  • Lin, Liyuan
  • Wang, Ruodu

Abstract

A useful property of independent samples is that their correlation remains the same after applying marginal transforms. This invariance property plays a fundamental role in statistical inference, but does not hold in general for dependent samples. In this paper, we study this invariance property on the Pearson correlation coefficient and its applications. A multivariate random vector is said to have an invariant correlation if its pairwise correlation coefficients remain unchanged under any common marginal transforms. For a bivariate case, we characterize all models of such a random vector via a certain combination of comonotonicity—the strongest form of positive dependence—and independence. In particular, we show that the class of exchangeable copulas with invariant correlation is precisely described by what we call positive Fréchet copulas. In the general multivariate case, we characterize the set of all invariant correlation matrices via the clique partition polytope. We also propose a positive regression dependent model that admits any prescribed invariant correlation matrix. Finally, we show that all our characterization results of invariant correlation, except one special case, remain the same if the common marginal transforms are confined to the set of increasing ones.

Suggested Citation

  • Koike, Takaaki & Lin, Liyuan & Wang, Ruodu, 2024. "Invariant correlation under marginal transforms," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:jmvana:v:204:y:2024:i:c:s0047259x2400068x
    DOI: 10.1016/j.jmva.2024.105361
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    References listed on IDEAS

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    1. Yang, Jingping & Qi, Yongcheng & Wang, Ruodu, 2009. "A class of multivariate copulas with bivariate Frechet marginal copulas," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 139-147, August.
    2. Wang, Bin & Wang, Ruodu & Wang, Yuming, 2019. "Compatible matrices of Spearman’s rank correlation," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 67-72.
    3. McNeil, Alexander J. & Nešlehová, Johanna G. & Smith, Andrew D., 2022. "On attainability of Kendall’s tau matrices and concordance signatures," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    4. Krause, Daniel & Scherer, Matthias & Schwinn, Jonas & Werner, Ralf, 2018. "Membership testing for Bernoulli and tail-dependence matrices," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 240-260.
    5. Yang, Jingping & Cheng, Shihong & Zhang, Lihong, 2006. "Bivariate copula decomposition in terms of comonotonicity, countermonotonicity and independence," Insurance: Mathematics and Economics, Elsevier, vol. 39(2), pages 267-284, October.
    6. Hofert, Marius & Koike, Takaaki, 2019. "Compatibility And Attainability Of Matrices Of Correlation-Based Measures Of Concordance," ASTIN Bulletin, Cambridge University Press, vol. 49(3), pages 885-918, September.
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