English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Slicing: Nonsingular Estimation of High Dimensional Covariance Matrices Using Multiway Kronecker Delta Covariance Structures