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

In data sets with many more features than observations, independent screeningbased on all univariate regression models leads to a computationally convenientvariable selection method. Recent efforts have shown that in the case ofgeneralized linear models, independent screening may suffice to capture allrelevant features with high probability, even in ultra-high dimension. It isunclear whether this formal sure screening property is attainable when theresponse is a right-censored survival time. We propose a computationally veryefficient independent screening method for survival data which can be viewed asthe natural survival equivalent of correlation screening. We state conditionsunder which the method admits the sure screening property within a generalclass of single-index hazard rate models with ultra-high dimensional features.An iterative variant is also described which combines screening with penalizedregression in order to handle more complex feature covariance structures. Themethods are evaluated through simulation studies and through application to areal gene expression dataset.

Independent screening for single-index hazard rate models with ultra-high dimensional features