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A Spearman dependence matrix for multivariate functional data

Author

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  • Francesca Ieva
  • Michael Ronzulli
  • Juan Romo
  • Anna Maria Paganoni

Abstract

We propose a nonparametric inferential framework for quantifying dependence among two families of multivariate functional data. We generalise the notion of Spearman correlation coefficient to situations where the observations are curves generated by a stochastic processes. In particular, several properties of the Spearman index are illustrated emphasising the importance of having a consistent estimator of the index. We use the notion of Spearman index to define the Spearman matrix, a mathematical object expressing the pattern of dependence among the components of a multivariate functional dataset. Finally, the notion of Spearman matrix is exploited to analyse two different populations of multivariate curves (specifically, Electrocardiographic signals of healthy and unhealthy people), in order to test if the pattern of dependence between the components is statistically different in the two cases.Abbreviations: ANA: anti-nuclear antibodies; APC: antigen-presenting cells; IRF:interferon regulatory factor

Suggested Citation

  • Francesca Ieva & Michael Ronzulli & Juan Romo & Anna Maria Paganoni, 2025. "A Spearman dependence matrix for multivariate functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 37(1), pages 82-104, January.
    • Handle: RePEc:taf:gnstxx:v:37:y:2025:i:1:p:82-104
    DOI: 10.1080/10485252.2024.2353615
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