On the robustness of least squares procedures in regression models
Abstract
The criterion robustness of the standard likelihood ratio test (LRT) under the multivariate normal regression model and also the inference robustness of the same test under the univariate set up are established for certain nonnormal distributions of errors. Restricting attention to the normal distribution of errors in the context of univariate regression models, conditions on the design matrix are established under which the usual LRT of a linear hypothesis (under homoscedasticity of errors) remains valid if the errors have an intraclass covariance structure. The conditions hold in the case of some standard designs. The relevance of C. R. Rao's (1967 In Proceedings Fifth Berkeley Symposium on Math. Stat. and Prob., Vol. 1, pp. 355-372) and G. Zyskind's (1967, Ann. Math. Statist.38 1092-1110) conditions in this context is discussed.Download Info
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Article provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 10 (1980)
Issue (Month): 3 (September)
Pages: 332-342
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Related research
Keywords: Univariate and multivariate regression models least squares procedures maximum likelihood estimates likelihood ratio tests intraclass correlation matrix balanced block designs;References
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Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Young, Dean M. & Seaman, John W. & Meaux, Laurie M., 1999. "Independence Distribution Preserving Covariance Structures for the Multivariate Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 68(2), pages 165-175, February.
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