On the Non Gaussian Asymptotics of the Likelihood Ratio Test Statistic for Homogeneity of Covariance
AbstractThe likelihood ratio test for m-sample homogeneity of covariance is notoriously sensitive to the violations of Gaussian assumptions. Its asymptotic behavior under non-Gaussian densities has been the subject of an abundant literature. In a recent paper, Yanagihara et al. (2005) show that the asymptotic distribution of the likelihood ratio test statistic, under arbitrary elliptical densities with finite fourth-order moments, is that of a linear combination of two mutually independent chi-square variables. Their proof is based on characteristic function methods, and only allows for convergence in distribution conclusions. Moreover, they require homokurticity among the m populations. Exploiting the findings of Hallin and Paindaveine (2008a), we reinforce that convergence-in-distribution result into a convergence-in- probability one —-that is, we explicitly decompose the likelihood ratio test statistic into a linear combination of two variables which are asymptotically independent chi-square —-and moreover extend it to the heterokurtic case.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number 2008_039.
Length: 9 p.
Date of creation: 2008
Date of revision:
Publication status: Published by:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-12-07 (All new papers)
- NEP-ECM-2008-12-07 (Econometrics)
- NEP-ORE-2008-12-07 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nagao, Hisao & Srivastava, M. S., 1992. "On the distributions of some test criteria for a covariance matrix under local alternatives and bootstrap approximations," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 331-350, November.
- Wakaki, Hirofumi & Eguchi, Shinto & Fujikoshi, Yasunori, 1990. "A class of tests for a general covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 313-325, February.
- Yanagihara, Hirokazu & Tonda, Tetsuji & Matsumoto, Chieko, 2005. "The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 237-264, October.
- Srivastava, M. S. & Khatri, C. G. & Carter, E. M., 1978. "On monotonicity of the modified likelihood ratio test for the equality of two covariances," Journal of Multivariate Analysis, Elsevier, vol. 8(2), pages 262-267, June.
- Arjun Gupta & Jin Xu, 2006. "On Some Tests of the Covariance Matrix Under General Conditions," Annals of the Institute of Statistical Mathematics, Springer, vol. 58(1), pages 101-114, March.
- Paindaveine, Davy, 2008. "A canonical definition of shape," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2240-2247, October.
- Hallin, Marc & Paindaveine, Davy, 2009. "Optimal tests for homogeneity of covariance, scale, and shape," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 422-444, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Benoit Pauwels).
If references are entirely missing, you can add them using this form.