A two-sample test for high-dimension, low-sample-size data under the strongly spiked eigenvalue model

DOI10.32917/HMJ/1509674448OpenAlexW2775322700WikidataQ128864858 ScholiaQ128864858MaRDI QIDQ1695764

Aki Ishii

Publication date: 8 February 2018

Published in: Hiroshima Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.hmj/1509674448

zbMATH Keywords

asymptotic distributionmicroarray dataHDLSSdistance-based two-sample testnoisereduction methodology

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Asymptotic distribution of eigenvalues, asymptotic theory of eigenfunctions for ordinary differential operators (34L20)

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A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context ⋮ A High-Dimensional Two-Sample Test for Non-Gaussian Data under a Strongly Spiked Eigenvalue Model ⋮ Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model

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