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- W4309182556 abstract "The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to various problems in two-sample testing. Towards this end, Bounliphone et al. (2016) used the theory of U-statistics to derive estimators for the variance of an MMD estimator, and differences between two such estimators. Their estimator, however, drops lower-order terms, and is unnecessarily biased. We show in this note - extending and correcting work of Sutherland et al. (2017) - that we can find a truly unbiased estimator for the actual variance of both the squared MMD estimator and the difference of two correlated squared MMD estimators, at essentially no additional computational cost." @default.
- W4309182556 created "2022-11-24" @default.
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- W4309182556 date "2019-06-05" @default.
- W4309182556 modified "2023-09-26" @default.
- W4309182556 title "Unbiased estimators for the variance of MMD estimators" @default.
- W4309182556 doi "https://doi.org/10.48550/arxiv.1906.02104" @default.
- W4309182556 hasPublicationYear "2019" @default.
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