Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space

DOI10.1016/j.spl.2019.108596zbMath1434.62028OpenAlexW2970082215WikidataQ127312980 ScholiaQ127312980MaRDI QIDQ2288753

Kanta Naito, Natsumi Makigusa

Publication date: 20 January 2020

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2019.108596

zbMATH Keywords

asymptotic normalityHilbert spacekernel methodmaximum mean discrepancy

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)

Related Items (3)

Asymptotic normality of a generalized maximum mean discrepancy estimator ⋮ Dimension-agnostic inference using cross U-statistics ⋮ On the rates of asymptotic normality for Bernstein polynomial estimators in a triangular array

Uses Software

• KernSmooth

Cites Work

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• Large sample theory for U-statistics and tests of fit
• On the effect of substituting parameter estimators in limiting \(\chi ^ 2U\) and V statistics
• A one-sample test for normality with kernel methods
• Modification of some goodness-of-fit statistics to yield asymptotically normal null distributions
• Approximation Theorems of Mathematical Statistics
• On the asymptotic normality of theL₂-Distance Class of Statistics with Estimated Parameters

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