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

We propose a data-analytic method for detecting cellwise outliers. Given arobust covariance matrix, outlying cells (entries) in a row are found by thecellHandler technique which combines lasso regression with a stepwiseapplication of constructed cutoff values. The penalty term of the lasso has aphysical interpretation as the total distance that suspicious cells need tomove in order to bring their row into the fold. For estimating a cellwiserobust covariance matrix we construct a detection-imputation method whichalternates between flagging outlying cells and updating the covariance matrixas in the EM algorithm. The proposed methods are illustrated by simulations andon real data about volatile organic compounds in children.

Handling cellwise outliers by sparse regression and robust covariance