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

Screening before model building is a reasonable strategy to reduce thedimension of regression problems. Sure independence screening is an efficientapproach to this purpose. It applies the slope estimate of a simple linearregression as a surrogate measure of the association between the response andthe predictor so that the final model can be built by those predictors withsteep slopes. However, if the response is truly affected by a nontrivial linearcombination of some predictors then the simple linear regression model is amisspecified model. In this work, we investigate the performance of the sureindependence screening in the view of model misspecification for binaryresponse regressions. Both maximum likelihood screening and least squarescreening are studied with the assumption that predictors follow multivariatenormal distribution and the true and the working link function belong to aclass of scale mixtures of normal distributions.

Variable Screenings in Binary Response Regressions with Multivariate Normal Predictors