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Largest magnitude for off-diagonal auto-correlation coefficients in high dimensional framework

Author

Listed:
  • Maxime Boucher

    (Université Libre de Bruxelles
    Université d’Orléans, Université de Tours, CNRS)

  • Didier Chauveau

    (Université d’Orléans, Université de Tours, CNRS)

  • Marguerite Zani

    (Université d’Orléans, Université de Tours, CNRS)

Abstract

This paper studies the coherence of an high-dimensional observations matrix. Specifically, we describe the limiting distribution of the largest magnitude of correlations matrix associated to our data outside a central band which size depends of the sample size. Using the Chen–Stein method, we show the convergence of the normalized coherence towards a Gumbel distribution. We broaden previous results by considering a 3-regime band structure for the off diagonal covariance matrix, where the largest band is composed of asymptotically vanishing coefficients. We provide an hypothesis test on the covariance structure where the alternative shows a clear dichotomy on the vanishing band. Moreover, we provide numerical simulations illustrating the asymptotic behavior of the coherence with Monte-Carlo experiment. We use a splitting strategy computing correlation matrices by blocks in order to avoid the high-dimensional memory issue.

Suggested Citation

  • Maxime Boucher & Didier Chauveau & Marguerite Zani, 2025. "Largest magnitude for off-diagonal auto-correlation coefficients in high dimensional framework," Statistical Papers, Springer, vol. 66(4), pages 1-40, June.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:4:d:10.1007_s00362-025-01693-y
    DOI: 10.1007/s00362-025-01693-y
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