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Nyström Kernel Stein Discrepancy Tests Accelerate Goodness-of-Fit Testing

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Researchers have developed Nyström Kernel Stein Discrepancy (KSD) tests that significantly improve the efficiency of goodness-of-fit testing. Traditional KSD estimators have quadratic runtime and computationally intractable null distributions, often requiring bootstrapping. This new method leverages the Nyström method to accelerate KSD estimation, preserving statistical accuracy and key properties like asymptotic level and local consistency. Numerical results show the Nyström-accelerated approach performs comparably to the original method while demanding substantially less runtime. AI

RANK_REASON The cluster contains an academic paper detailing a new statistical method. [lever_c_demoted from research: ic=2 ai=0.4]

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Nyström Kernel Stein Discrepancy Tests Accelerate Goodness-of-Fit Testing

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Florian Kalinke, Zolt\'an Szab\'o, Bharath K. Sriperumbudur ·

    Nystr\"om Kernel Stein Discrepancy Tests

    arXiv:2605.25173v1 Announce Type: cross Abstract: Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deployments. One of the main applications of KSD is in constructing powerful GoF tests. H…

  2. arXiv stat.ML TIER_1 Deutsch(DE) · Bharath K. Sriperumbudur ·

    Nyström Kernel Stein Discrepancy Tests

    Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deployments. One of the main applications of KSD is in constructing powerful GoF tests. However, tests relying on the classical U-/V-statis…

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