  Nonsingular estimation of high dimensional covariance matrices is an
important step in many statistical procedures like classification, clustering,
variable selection an future extraction. After a review of the essential
background material, this paper introduces a technique we call slicing for
obtaining a nonsingular covariance matrix of high dimensional data. Slicing is
essentially assuming that the data has Kronecker delta covariance structure.
Finally, we discuss the implications of the results in this paper and provide
an example of classification for high dimensional gene expression data.
