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/

Zendy Plus

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

Zendy Tools

Zendy Open

The geometric median covariation matrix is a robust multivariate indicator ofdispersion which can be extended without any difficulty to functional data. Wedefine estimators, based on recursive algorithms, that can be simply updated ateach new observation and are able to deal rapidly with large samples of highdimensional data without being obliged to store all the data in memory.Asymptotic convergence properties of the recursive algorithms are studied underweak conditions. The computation of the principal components can also beperformed online and this approach can be useful for online outlier detection.A simulation study clearly shows that this robust indicator is a competitivealternative to minimum covariance determinant when the dimension of the data issmall and robust principal components analysis based on projection pursuit andspherical projections for high dimension data. An illustration on a largesample and high dimensional dataset consisting of individual TV audiencesmeasured at a minute scale over a period of 24 hours confirms the interest ofconsidering the robust principal components analysis based on the mediancovariation matrix. All studied algorithms are available in the R packageGmedian on CRAN.

Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis