Open Access
November 2020 High-dimensional general linear hypothesis tests via non-linear spectral shrinkage
Haoran Li, Alexander Aue, Debashis Paul
Bernoulli 26(4): 2541-2571 (November 2020). DOI: 10.3150/19-BEJ1186


We are interested in testing general linear hypotheses in a high-dimensional multivariate linear regression model. The framework includes many well-studied problems such as two-sample tests for equality of population means, MANOVA and others as special cases. A family of rotation-invariant tests is proposed that involves a flexible spectral shrinkage scheme applied to the sample error covariance matrix. The asymptotic normality of the test statistic under the null hypothesis is derived in the setting where dimensionality is comparable to sample sizes, assuming the existence of certain moments for the observations. The asymptotic power of the proposed test is studied under various local alternatives. The power characteristics are then utilized to propose a data-driven selection of the spectral shrinkage function. As an illustration of the general theory, we construct a family of tests involving ridge-type regularization and suggest possible extensions to more complex regularizers. A simulation study is carried out to examine the numerical performance of the proposed tests.


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Haoran Li. Alexander Aue. Debashis Paul. "High-dimensional general linear hypothesis tests via non-linear spectral shrinkage." Bernoulli 26 (4) 2541 - 2571, November 2020.


Received: 1 September 2018; Revised: 1 October 2019; Published: November 2020
First available in Project Euclid: 27 August 2020

zbMATH: 07256152
MathSciNet: MR4140521
Digital Object Identifier: 10.3150/19-BEJ1186

Keywords: general linear hypothesis , local alternatives , Random matrix theory , ridge shrinkage , spectral shrinkage

Rights: Copyright © 2020 Bernoulli Society for Mathematical Statistics and Probability

Vol.26 • No. 4 • November 2020
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