Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0047-259X(03)00056-3
Title: A chi-square test for dimensionality with non-Gaussian data
Authors: Bai, Z.D. 
He, X.
Keywords: Canonical correlation
Chi-square test
Dimension reduction
Inverse regression
SIR models
Issue Date: Jan-2004
Citation: Bai, Z.D., He, X. (2004-01). A chi-square test for dimensionality with non-Gaussian data. Journal of Multivariate Analysis 88 (1) : 109-117. ScholarBank@NUS Repository. https://doi.org/10.1016/S0047-259X(03)00056-3
Abstract: The classical theory for testing the null hypothesis that a set of canonical correlation coefficients is zero leads to a chi-square test under the assumption of multi-normality. The test has been used in the context of dimension reduction. In this paper, we study the limiting distribution of the test statistic without the normality assumption, and obtain a necessary and sufficient condition for the chi-square limiting distribution to hold. Implications of the result are also discussed for the problem of dimension reduction. © 2003 Elsevier Science (USA). All rights reserved.
Source Title: Journal of Multivariate Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/104923
ISSN: 0047259X
DOI: 10.1016/S0047-259X(03)00056-3
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