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<p>The authors propose a measure termed the kernel Pearson correlation coefficient, which can be conceptualized as a nonparametric extension of the traditional Pearson correlation coefficient within the framework of a reproducing kernel Hilbert space. This methodology offers several desirable benefits including eliminating the necessity for model assumptions, being well-suited for high-dimensional data, and being adaptable to diverse data structures with a suitable kernel. The authors validate its robust statistical properties through simulations and demonstrate its effectiveness through a practical application involving the host transcriptome and microbiome data.</p>

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Kernel Pearson Correlation Coefficient Test for Identifying Independence of Two High-Dimensional Random Vectors

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Yuke Shi,
  • Zhenzhen Jiang

摘要

The authors propose a measure termed the kernel Pearson correlation coefficient, which can be conceptualized as a nonparametric extension of the traditional Pearson correlation coefficient within the framework of a reproducing kernel Hilbert space. This methodology offers several desirable benefits including eliminating the necessity for model assumptions, being well-suited for high-dimensional data, and being adaptable to diverse data structures with a suitable kernel. The authors validate its robust statistical properties through simulations and demonstrate its effectiveness through a practical application involving the host transcriptome and microbiome data.