In:
Biometrika, Oxford University Press (OUP), Vol. 110, No. 3 ( 2023-08-14), p. 777-797
Abstract:
In this paper, we develop a systematic theory for high-dimensional analysis of variance in multivariate linear regression, where the dimension and the number of coefficients can both grow with the sample size. We propose a new U-type statistic to test linear hypotheses and establish a high-dimensional Gaussian approximation result under fairly mild moment assumptions. Our general framework and theory can be used to deal with the classical one-way multivariate analysis of variance, and the nonparametric one-way multivariate analysis of variance in high dimensions. To implement the test procedure, we introduce a sample-splitting-based estimator of the second moment of the error covariance and discuss its properties. A simulation study shows that our proposed test outperforms some existing tests in various settings.
Type of Medium:
Online Resource
ISSN:
0006-3444
,
1464-3510
DOI:
10.1093/biomet/asad001
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2023
detail.hit.zdb_id:
1119-8
detail.hit.zdb_id:
1470319-1
SSG:
12
Permalink
