On the Studentisation of Random Vectors

We give general matrix Studentisation results for random vectors converging in distribution to a spherically symmetric random vector, which have wide applicability to the asymptotic properties of estimators obtained from estimating equations, for example. Appropriate matrix "square roots," required for normalisation of the random vectors, are shown to be the Cholesky square root and the symmetric positive definite square root.

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Year of publication:	1996
Authors:	Vu, H. T. V. ; Maller, R. A. ; Klass, M. J.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 57.1996, 1, p. 142-155
Publisher:	Elsevier
Keywords:	Studentisation random vectors spherically symmetric random vectors norming matrices stochastic matrices non-stochastic matrices positive definite matrices Cholesky square root symmetric square root (null)

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005152948