JSM 2004 Online Program

Abstract #300477

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Activity Number:	151
Type:	Topic Contributed
Date/Time:	Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Biometrics Section
Abstract - #300477
Title:	Multivariate Skew-normal Distributions and Maxima of Normally Distributed Random Vectors
Author(s):	Nicola M.R. Lopefido*+
Companies:	Università di Urbino
Address:	Facolta' di Economia, Urbino (PU), International, 61033, Italy
Keywords:	skewness ; extremes ; invariance
Abstract:	Several authors showed that the minimum (maximum) of a bivariate normal vector with standardized marginals is univariate skew-normal. The present work shows that extensions of this result to normal vectors in higher dimensions lead to several generalizations of the univariate skew-normal: the multivariate skew-normal, the general skew-normal, and the Fundamental skew-normal. From the theoretical viewpoint, the above results imply a new invariance property of skew-normal vectors. From the applied viewpoint, these results are useful in measuring visual acuity, hearing loss function and lungs' efficiency.

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