Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T02:34:34.322Z Has data issue: false hasContentIssue false

Characterizations of r-potent matrices

Published online by Cambridge University Press:  24 October 2008

Joseph P. McCloskey
Affiliation:
Department of Mathematics and Computer Science, University of Maryland Baltimore County, Catonville, MD 21228, U.S.A.

Extract

A matrix A is said to be tripotent whenever A3 = A. The study of tripotent matrices is of statistical interest since if the n × 1 real random vector X follows an N(0, I) distribution and A is a symmetric matrix then the real quadratic form X′AX is distributed as the difference of two independently distributed X2 variates if and only if A3 = A. In fact, a necessary and sufficient condition that A is tripotent is that there exist two idempotent matrices B and C such that A = BC, and BC = 0. Using properties of diagonalizable matrices, we will prove several algebraic characterizations of r-potent matrices that extend the known results for tripotent matrices. Our first result will be to obtain an analogous decomposition for an arbitrary r-potent matrix.

Type
Research Article
Copyright
Copyright © Cambridge Philosophical Society 1984

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Anderson, T. W. and Styan, G. P. H.. Cochran’s Theorem, Rank Additivity, and Tripotent Matrices. Technical Report 43, Department of Statistics, Stanford University, (1980)Google Scholar
[2]Chipman, J. S. and Rao, M. M.. Projections, generalized inverses, and quadratic forms. J. Math. Anal. Appl. 9 (1964), 111.CrossRefGoogle Scholar
[3]Cochran, W. G.. The distribution of quadratic forms in a normal system with applications to the analysis of covariance. Proc. Cambridge Philos. Soc. 30 (1934), 178181.CrossRefGoogle Scholar
[4]Drazin, M. P.. On diagonable and normal matrices. Quart. J. Math. 2 (1951), 189198.CrossRefGoogle Scholar
[5]Graybill, F. A.. Matrices with Applications in Statistics (Wadsworth Publishing Company, 1983).Google Scholar
[6]Graybill, F. A. and Marsaglia, G.. Idempotent matrices and quadratic forms in the general linear hypothesis. Ann. Statist. 28 (1957), 676686.Google Scholar
[7]Khatri, C. G.. Some results for the singular multivariate regression models. Sankhyä 30 (1968), 267280.Google Scholar
[8]Perlis, S.. Theory of Matrices (Addison-Wesley, 1958).Google Scholar
[9]Pittenger, A. O., Personal communication, 1982.Google Scholar
[10]Rao, C. R. and Mitra, S. K.. Generalized Inverse of Matrices and Its Applications. (John Wiley, 1971).Google Scholar
[11]Styan, G. P. H.. Notes on the distribution of quadratic forms in singular normal variates. Biometrika 57 (1970), 567572.CrossRefGoogle Scholar