Simultaneous testing of mean vector and covariance matrix for high-dimensional data

DOI10.1016/j.jspi.2017.03.009zbMath1391.62098OpenAlexW2604257403MaRDI QIDQ2407080

Ning-Zhong Shi, Zhongying Liu, Bai-Sen Liu, Shurong Zheng

Publication date: 28 September 2017

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2017.03.009

zbMATH Keywords

martingale difference sequencesimultaneous testhigh-dimensional covariance matrixhigh-dimensional mean vectortheoretical power function

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15)

Related Items (9)

A simultaneous test of mean vector and covariance matrix in high-dimensional settings ⋮ Hotelling's \(T^2\) in separable Hilbert spaces ⋮ Simultaneous testing of the mean vector and covariance matrix among k populations for high-dimensional data ⋮ A simultaneous testing of the mean vector and the covariance matrix among two populations for high-dimensional data ⋮ A p-value based dimensionality reduction test for high dimensional means ⋮ Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covariance Matrices with Application to Gene-Set Testing ⋮ On LR simultaneous test of high-dimensional mean vector and covariance matrix under non-normality ⋮ Inference for high-dimensional split-plot-designs: a unified approach for small to large numbers of factor levels ⋮ Projected tests for high-dimensional covariance matrices

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• Properties of Power Functions of Some Tests Concerning Dispersion Matrices of Multivariate Normal Distributions
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