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New Martingale Test Speeds Up Variable Independence Checks 60x

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new statistical test called the Martingale Kernel Independence Test (mHSIC and mdHSIC) to efficiently assess the independence of variables. This new method offers a significant speedup, running 25 to 60 times faster than existing permutation-based tests by replacing computationally intensive permutation steps with a single normal-quantile lookup. The mHSIC statistic achieves quadratic cost and is consistent against all fixed alternatives, while the mdHSIC statistic offers finite-sample consistency with a linear cost in the number of tested variables. AI

IMPACT Introduces a faster statistical test for variable independence, potentially accelerating research and model development that relies on such analyses.

RANK_REASON The cluster contains an academic paper detailing a new statistical method.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Felix Laumann, Zhaolu Liu, Mauricio Barahona ·

    A Martingale Kernel Independence Test

    arXiv:2605.22549v1 Announce Type: new Abstract: The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension $d\mathrm{HSIC}$ are degenerate $V$-statistics whose data-dependent weighted-$\chi^2$ null limits force a permutation calibration that multiplies…

  2. arXiv stat.ML TIER_1 English(EN) · Mauricio Barahona ·

    A Martingale Kernel Independence Test

    The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension $d\mathrm{HSIC}$ are degenerate $V$-statistics whose data-dependent weighted-$χ^2$ null limits force a permutation calibration that multiplies the per-test cost by the number of permutations, i…

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