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Visualization of Correlation Tables by Positive/Negative Threshold for Coefficients Significance

Author

Listed:
  • Mullat, Joseph

Abstract

This work introduces a novel approach to the visualization of correlation tables, guided by positive and negative significance thresholds of correlation coefficients. Traditional methods for visualizing correlation matrices often rely on heuristic color schemes, which lack a robust analytical foundation. To address this limitation, we propose a method that constructs and analyzes an ordered sequence of so called momentums, separating positive and negative correlations based on their significance. By leveraging mathematical principles of an optimal solutions, this approach enhances the clarity and interpretability of correlation patterns.

Suggested Citation

  • Mullat, Joseph, 2025. "Visualization of Correlation Tables by Positive/Negative Threshold for Coefficients Significance," MPRA Paper 123910, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:123910
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    File URL: https://mpra.ub.uni-muenchen.de/123910/1/MPRA_paper_123910.pdf
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    References listed on IDEAS

    as
    1. Cho, Wendy K. Tam & Liu, Yan Y., 2018. "Sampling from complicated and unknown distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 170-178.
    2. Ishii, Aki & Yata, Kazuyoshi & Aoshima, Makoto, 2022. "Geometric classifiers for high-dimensional noisy data," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
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    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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