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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A new model-free variable selection method was proposed in this article, which is called SMAVE-AdEN. We combined the effective sufficient dimension reduction method MAVE with the variable selection method which is called adaptive Elastic Net (AdEN) to introduce SMAVE-AdEN. The SMAVE-AdEN produces a sparse and accurate estimate when the predictors are highly correlated. The advantage of SMAVE-AdEN is that SMAVE-AdEN extended Adaptive Elastic net (AdEN) to nonlinear and multi-dimensional regression. Also, the SMAVE-AdEN enables MAVE to work with problems were the predictors are highly correlated. In addition, SMAVE-AdEN can exhaustively estimate dimensions, while selecting informative covariates simultaneously. The performance of SMAVE-AdEN is evaluated by both simulation and real data analysis.

Sparse minimum average variance estimation via the adaptive elastic net when the predictors correlated