Multivariate regression analysis with dependent observations: conditions for the invariance of the distribution of the Lawley-Hotelling test for the model utility

Laurie M. Meaux; Dean M. Young; John W. Seaman Jr.

doi:10.6092/issn.1973-2201/675

Multivariate regression analysis with dependent observations: conditions for the invariance of the distribution of the Lawley-Hotelling test for the model utility

Authors

Laurie M. Meaux University of Arkansas
Department of Mathematical Science
Dean M. Young Baylor University, Texas
Department of Information System
John W. Seaman Jr. Baylor University, Texas
Department of Information System

DOI:

https://doi.org/10.6092/issn.1973-2201/675

Abstract

In this paper we derive necessary and sufficient conditions on the error covariance structure such that the Lawley-Hotelling statistic for testing model utility is invariant from its distribution under the usual assumption of normal i.i.d. observation vectors. We, therefore, generalize the work of Arnold (1979), Ghosh and Sinha (1980), and Tranquilli and Baldesarri (1988).

How to Cite

Meaux, L. M., Young, D. M., & Seaman Jr., J. W. (1994). Multivariate regression analysis with dependent observations: conditions for the invariance of the distribution of the Lawley-Hotelling test for the model utility. Statistica, 54(2), 139–150. https://doi.org/10.6092/issn.1973-2201/675