Let $Z$ be a random vector whose distribution is spherically symmetric about the origin. A random vector $X$ which is representable as the image of $Z$ under affine transformation is said to have an ellipsoidally symmetric distribution. The model of ellipsoidal symmetry is a useful generalization of multivariate normality. This paper proposes and studies some goodness-of-fit tests which have good asymptotic power over a broad spectrum of alternatives to ellipsoidal symmetry.
Publié le : 1979-01-14
Classification:
Ellipsoidal symmetry,
spherical symmetry,
goodness-of-fit test,
multivariate density estimator,
dependent central limit theorem,
62G10,
62E20
@article{1176344561,
author = {Beran, Rudolf},
title = {Testing for Ellipsoidal Symmetry of a Multivariate Density},
journal = {Ann. Statist.},
volume = {7},
number = {1},
year = {1979},
pages = { 150-162},
language = {en},
url = {http://dml.mathdoc.fr/item/1176344561}
}
Beran, Rudolf. Testing for Ellipsoidal Symmetry of a Multivariate Density. Ann. Statist., Tome 7 (1979) no. 1, pp. 150-162. http://gdmltest.u-ga.fr/item/1176344561/