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stat.ML2023
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Xing Liu, Andrew B. Duncan, Axel Gandy
Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising…
math.ST2023★ 1 cited
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing
Kevin H. Huang, Xing Liu, Andrew B. Duncan +1
We prove a convergence theorem for U-statistics of degree two, where the data dimension is allowed to scale with sample size . We find that the limiting distribution of a U-…