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-statistic-based KSD estimators have two major drawbacks. (i) Their runtime scales quadratically in the number of samples. (ii) Their asymptotic null distribution is computationally intractable in most cases, typically handled by bootstrapping. While it is known that the Nyström method permits accelerating KSD estimation with no loss of statistical accuracy under mild conditions, to the best of our knowledge, the fundame...
| Authors | Florian Kalinke, Zoltán Szabó, Bharath K. Sriperumbudur |
|---|---|
| Primary category | stat.ML |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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