Literature DB >> 30034063

Screening group variables in the proportional hazards model.

Kwang Woo Ahn1, Natasha Sahr1, Soyoung Kim1.   

Abstract

We propose a method to screen group variables under the high dimensional group variable setting for the proportional hazards model. We study the sure screening property of the proposed method for independent and clustered survival data. The simulation study shows that the proposed method performs better for group variable screening than some existing procedures.

Entities:  

Keywords:  High dimensional data; Joint screening; Proportional hazards model; Sure screening property

Year:  2017        PMID: 30034063      PMCID: PMC6051756          DOI: 10.1016/j.spl.2017.11.014

Source DB:  PubMed          Journal:  Stat Probab Lett        ISSN: 0167-7152            Impact factor:   0.870


  4 in total

1.  Principled sure independence screening for Cox models with ultra-high-dimensional covariates.

Authors:  Sihai Dave Zhao; Yi Li
Journal:  J Multivar Anal       Date:  2012-02-01       Impact factor: 1.473

2.  Discussion of "Sure Independence Screening for Ultra-High Dimensional Feature Space.

Authors:  Hao Helen Zhang
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2008-11       Impact factor: 4.488

3.  Feature Screening in Ultrahigh Dimensional Cox's Model.

Authors:  Guangren Yang; Ye Yu; Runze Li; Anne Buu
Journal:  Stat Sin       Date:  2016       Impact factor: 1.261

4.  ORACLE INEQUALITIES FOR THE LASSO IN THE COX MODEL.

Authors:  Jian Huang; Tingni Sun; Zhiliang Ying; Yi Yu; Cun-Hui Zhang
Journal:  Ann Stat       Date:  2013-06-01       Impact factor: 4.028

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.