A one-sample test for normality with kernel methods

DOI10.3150/18-BEJ1037zbMath1466.62340arXiv1507.02904MaRDI QIDQ2419660

Jérémie Kellner, Alain Celisse

Publication date: 14 June 2019

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.02904

zbMATH Keywords

kernel methodsreproducing kernel Hilbert spaceparametric bootstrapmaximum mean discrepancynormality test

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (8)

On combining the zero bias transform and the empirical characteristic function to test normality ⋮ A one-sample test for normality with kernel methods ⋮ Unnamed Item ⋮ Dimension-agnostic inference using cross U-statistics ⋮ Asymptotics and practical aspects of testing normality with kernel methods ⋮ Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space ⋮ Tests for multivariate normality -- a critical review with emphasis on weighted $L^2$-statistics ⋮ A review of goodness-of-fit tests for models involving functional data

Uses Software

• spcov

Cites Work

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