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

Sparse modeling plays a ubiquitous role in modern statistical regression. In particular, high-dimensional survival analysis has drawn a lot of attention as a result of the popularity of microarray studies involving survival data. In this paper, we focus on a scenario where predictors are strongly correlated, also known as grouping effect, which is highly desirable when analysing high-dimensional microarray data. To perform simultaneous variable selection and estimation under this circumstance, we propose the-regularized best-subsets estimator under the framework of additive hazards models based on a polynomial algorithm for the best subset selection. Moreover, we establish comprehensive statistical properties, including oracle inequalities under estimation loss for the proposed estimator. The proposed method is demonstrated by simulation studies and illustrated by a real data example.

Best subset selection with shrinkage: sparse additive hazards regression with the grouping effect