On two-sample mean tests under spiked covariances

DOI10.1016/j.jmva.2018.05.004zbMath1398.62147OpenAlexW2803447629WikidataQ129764331 ScholiaQ129764331MaRDI QIDQ1661347

Rui Wang, Xingzhong Xu

Publication date: 16 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2018.05.004

zbMATH Keywords

principal subspacehigh dimensionspiked covariance modelmean test

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15)

Related Items (6)

A simultaneous test of mean vector and covariance matrix in high-dimensional settings ⋮ A high-dimensional test on linear hypothesis of means under a low-dimensional factor model ⋮ Linear Hypothesis Testing in Linear Models With High-Dimensional Responses ⋮ Unnamed Item ⋮ Recent developments in high-dimensional inference for multivariate data: parametric, semiparametric and nonparametric approaches ⋮ A high-dimensional test for multivariate analysis of variance under a low-dimensional factor structure

Cites Work

• Unnamed Item
• Unnamed Item
• Minimax bounds for sparse PCA with noisy high-dimensional data
• A high dimensional two-sample test under a low dimensional factor structure
• PCA consistency in high dimension, low sample size context
• A high-dimensional two-sample test for the mean using random subspaces
• A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\)
• Asymptotics of empirical eigenstructure for high dimensional spiked covariance
• Optimal estimation and rank detection for sparse spiked covariance matrices
• A two-sample test for high-dimensional data with applications to gene-set testing
• Sparse PCA: optimal rates and adaptive estimation
• A test for the mean vector with fewer observations than the dimension
• Eigenvalue Ratio Test for the Number of Factors
• A Regularized Hotelling’sT²Test for Pathway Analysis in Proteomic Studies
• On Estimation of the Noise Variance in High Dimensional Probabilistic Principal Component Analysis
• Determining the Number of Factors in Approximate Factor Models
• Asymptotic Theory for Principal Component Analysis

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