Limiting behavior of eigenvalues in high-dimensional MANOVA via RMT
type: Item
instance of: scholarly article
title: Limiting behavior of eigenvalues in high-dimensional MANOVA via RMT
zbMATH Open document ID: 1411.62130
DOI: 10.1214/17-AOS1646
author: Yasunori Fujikoshi
author: Zhidong Bai
author: Kwok Pui Choi
published in: The Annals of Statistics
publication date: 2018-10-30T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.aos/1536307240
review text: The authors derive the asymptotic joint distribution of the eigenvalues under the null case and the local alternative cases in the MANOVA model and multiple discriminant analysis when both the dimension and the sample size are large. They apply methods from random matrix theory and do not assume normality of the population. It was shown that the null and nonnull distributions of the eigenvalues and invariant test statistics are asymptotically robust against departure from normality in high-dimensional situations.
Mathematics Subject Classification ID: 62H10
Mathematics Subject Classification ID: 62E20
Mathematics Subject Classification ID: 62J10
zbMATH DE Number: 6968606
zbMATH Keywords: asymptotic distribution
zbMATH Keywords: eigenvalues
zbMATH Keywords: discriminant analysis
zbMATH Keywords: high-dimensional case
zbMATH Keywords: MANOVA
zbMATH Keywords: nonnormality
zbMATH Keywords: RMT
zbMATH Keywords: test statistics
reviewed by: B. L. S. Prakasa Rao
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6968606