by Läuter, J,, Glimm, E., Kropf, S..
Series: 1997-06, Preprints
this paper, a method for multivariate testing based on low-dimensional, data-
dependent, linear scores is proposed. The new approach reduces the dimensionality
of observations and increases the stability of the solutions. The method is reliable,
even if there are many redundant variables. As a key feature, the score coefficients
can be chosen such that a left-spherical distribution of the scores is reached under the
null hypothesis. Therefore, well-known tests become applicable in high-dimensional
situations, too. The presented strategy is an alternative to least squares and max-
imum likelihood approaches. In a natural way, standard problems of multivari-
ate analysis thus induce the occurrence of left-spherical, non-normal distributions.
Hence, new fields of application are opened up to the generalized multivariate anal-
ysis. The proposed methodology is not restricted to normally distributed data, but
can also be extended to any left-spherically distributed observations.
Multivariate test, linear scores, spherical distribution, generalized multivariateanalysis, exact test, null robustness.