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Clustering-based inter-regional correlation estimation

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  • Lbath, Hanâ
  • Petersen, Alexander
  • Meiring, Wendy
  • Achard, Sophie

Abstract

A novel non-parametric estimator of the correlation between grouped measurements of a quantity is proposed in the presence of noise. The main motivation is functional brain network construction from fMRI data, where brain regions correspond to groups of spatial units, and correlation between region pairs defines the network. The challenge resides in the fact that both noise and intra-regional correlation lead to inconsistent inter-regional correlation estimation using classical approaches. While some existing methods handle either one of these issues, no non-parametric approaches tackle both simultaneously. To address this problem, a trade-off between two procedures is proposed: correlating regional averages, which is not robust to intra-regional correlation; and averaging pairwise inter-regional correlations, which is not robust to noise. To that end, the data is projected onto a space where Euclidean distance is used as a proxy for sample correlation. Hierarchical clustering is then leveraged to gather together highly correlated variables within each region prior to inter-regional correlation estimation. The convergence of the proposed estimator is analyzed, and the proposed approach is empirically shown to surpass several other popular methods in terms of quality. Illustrations on real-world datasets that further demonstrate its effectiveness are provided.

Suggested Citation

  • Lbath, Hanâ & Petersen, Alexander & Meiring, Wendy & Achard, Sophie, 2024. "Clustering-based inter-regional correlation estimation," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:csdana:v:191:y:2024:i:c:s0167947323001871
    DOI: 10.1016/j.csda.2023.107876
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    References listed on IDEAS

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    1. Chavent, Marie & Kuentz-Simonet, Vanessa & Liquet, Benoît & Saracco, Jérôme, 2012. "ClustOfVar: An R Package for the Clustering of Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i13).
    2. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    3. B. Rosner & A. Donner & C. H. Hennekens, 1977. "Estimation of Interclass Correlation from Familial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(2), pages 179-187, June.
    4. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
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