An overview of tests on high-dimensional means

DOI10.1016/j.jmva.2021.104813zbMath1493.62342OpenAlexW3197173810MaRDI QIDQ2062768

Changcheng Li, Song-Shan Yang, Run-Ze Li, Yu-An Huang

Publication date: 3 January 2022

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104813

zbMATH Keywords

regularization methodmultiple comparisonHotelling's \(T^2\) testprojection test

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Hypothesis testing in multivariate analysis (62H15)

Related Items

Finite sample \(t\)-tests for high-dimensional means, Projection Test for Mean Vector in High Dimensions

Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• Nearly unbiased variable selection under minimax concave penalty
• An adaptable generalization of Hotelling's $T^2$ test in high dimension
• Higher criticism for large-scale inference, especially for rare and weak effects
• A simpler spatial-sign-based two-sample test for high-dimensional data
• A nonparametric two-sample test applicable to high dimensional data
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• A test for the mean vector with fewer observations than the dimension under non-normality
• A high-dimensional two-sample test for the mean using random subspaces
• Multivariate tests based on left-spherically distributed linear scores
• Robust multivariate nonparametric tests via projection averaging
• Tests for high-dimensional data based on means, spatial signs and spatial ranks
• Distribution and correlation-free two-sample test of high-dimensional means
• Linear hypothesis testing for high dimensional generalized linear models
• A two-sample test for high-dimensional data with applications to gene-set testing
• Two-sample and ANOVA tests for high dimensional means
• High dimensional two-sample test based on the inter-point distance
• Tests alternative to higher criticism for high-dimensional means under sparsity and column-wise dependence
• A test for the mean vector with fewer observations than the dimension
• Asymptotically independent U-statistics in high-dimensional testing
• A Regularized Hotelling’sT²Test for Pathway Analysis in Proteomic Studies
• Exact t and F Tests for Analyzing Studies with Multiple Endpoints
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Simulation‐based hypothesis testing of high dimensional means under covariance heterogeneity
• On the Power Behaviour of Läuter's Exact Multivariate One‐sided Tests
• Two-Sample Test of High Dimensional Means Under Dependence
• Statistical Foundations of Data Science
• Empirical likelihood test for a large-dimensional mean vector
• A Simple Two-Sample Test in High Dimensions Based on L²-Norm
• A Two-Sample Test for Equality of Means in High Dimension
• A High-Dimensional Nonparametric Multivariate Test for Mean Vector
• An adaptive two-sample test for high-dimensional means
• Two-sample tests of high-dimensional means for compositional data
• A Significance Test for the Separation of Two Highly Multivariate Small Samples
• A High Dimensional Two Sample Significance Test
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models