Using projection pursuit techniques, in this paper we propose a procedure to detect multiple
outliers in multivariate data. The basic idea behind this procedure is to project the
multivariate data to univariate observations and then to apply an appropriate univariate
outlier identifier to the projected data. The projected outlier identifier forms a centered
Gaussian process on the high-dimensional unit sphere. When a set of directions is
generated on the unit sphere, the projected outlier identifier on these directions then follows …