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The Concentration Ellipsoid of a Random Vector Revisited

Published online by Cambridge University Press:  11 February 2009

Abstract

Alternative definitions of the concentration ellipsoid of a random vector are surveyed, and an extension of the concentration ellipsoid of Darmois is suggested as being the most convenient and natural definition. The advantage of the proposed definition in providing substantially simplified proofs of results in (linear) estimation theory is discussed, and is illustrated by new and short proofs of two key results. A not-so-well-known, but elementary, extremal representation of a nonnegative definite quadratic form, together with the corresponding Cauchy-Schwarẓ-type inequality, is seen to play a crucial role in these proofs.

Type
Brief Report
Copyright
Copyright © Cambridge University Press 1991

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References

REFERENCES

1. Anderson, T.W. An Introduction to Multivariate Statistical Analysis. Second Edition. New York: Wiley, 1984.Google Scholar
2. Cramér, H. A contribution to the theory of statistical estimation. Skandinavisk Aktuarietidskrift 29 (1946): 8594.Google Scholar
3. Cramér, H. Mathematical Methods of Statistics. Princeton: Princeton University Press, 1946.Google Scholar
4. Darmois, G. Sur les limites de la dispersion de certaines estimations. Revue de I'Jnstitut International de Statistique 13 (1945): 915.CrossRefGoogle Scholar
5. Drygas, H. On a generalization of the Farkas theorem. Zeitschrift fur Unternehmensforschung 13 (1969): 283290.Google Scholar
6. Drygas, H. The Coordinate-Free Approach to Gauss-Markov Estimation. Berlin: Springer, 1970.CrossRefGoogle Scholar
7. Karlin, S. & Studden, W.J.. Optimal experimental designs. Annals of Mathematical Statistics 37 (1966): 783815.CrossRefGoogle Scholar
8. Lehmann, E.L. & Scheffé, H.. Completeness, similar regions, and unbiased estimation – part I. Sankhyā 10 (1950): 305340.Google Scholar
9. Malinvaud, E. Me'thodes Statistiques de l'Économétrie. 3e édition. Paris: Dunod, 1981.Google Scholar
.10 Nordström, K. Some further aspects of the Lowner-ordering antitonicity of the Moore- Penrose inverse. Communications in Statistics Theory and Methods 18 (1989): 44714489.CrossRefGoogle Scholar
.11 Phillips, P.C.B. The concentration ellipsoid of a random vector. Journal of Econometrics 11 (1979): 363365.CrossRefGoogle Scholar
.12 Philoche, J.-L. A propos du théorème de Gauss-Markov. Annales de I'Institut Henri Poincaré Section B: Calcul des Probabilités et Statistique VII (1971): 271281.Google Scholar