Hypothesis Testing in High-Dimensional Linear Regression: A Normal Reference Scale-Invariant Test

DOI10.5705/ss.202020.0362OpenAlexW4200014520WikidataQ114013817 ScholiaQ114013817MaRDI QIDQ5041337

Tianming Zhu, Liang Zhang, Jin-Ting Zhang

Publication date: 13 October 2022

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202020.0362

zbMATH Keywords

high-dimensional linear regressiongeneral linear hypothesis testingscale-invariant test

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Linear hypothesis testing in high-dimensional one-way MANOVA
• Multivariate analysis of variance with fewer observations than the dimension
• Testing the equality of several covariance matrices with fewer observations than the dimension
• Robust principal component analysis for functional data. (With comments)
• Tests for multivariate analysis of variance in high dimension under non-normality
• High-dimensional general linear hypothesis tests via non-linear spectral shrinkage
• Some high-dimensional tests for a one-way MANOVA
• A Test for Multivariate Analysis of Variance in High Dimension
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• A Simple Two-Sample Test in High Dimensions Based on L²-Norm
• Multivariate Theory for Analyzing High Dimensional Data

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