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 an l1-regularized likelihood method for estimating the inversecovariance matrix in the high-dimensional multivariate normal model in presenceof missing data. Our method is based on the assumption that the data aremissing at random (MAR) which entails also the completely missing at randomcase. The implementation of the method is non-trivial as the observed negativelog-likelihood generally is a complicated and non-convex function. We proposean efficient EM algorithm for optimization with provable numerical convergenceproperties. Furthermore, we extend the methodology to handle missing values ina sparse regression context. We demonstrate both methods on simulated and realdata.

Missing values: sparse inverse covariance estimation and an extension to sparse regression