Feature Selection in High-Dimensional Survival Data with Linear Regression

来源 :上海交通大学 | 被引量 : 0次 | 上传用户:hyp88_down
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
  Variable selection from the high-dimensional survival data is a fundamental and challenging approach in recent years.
其他文献
Estimating a patients mortality risk is important in making treatment decisions.Treebased methods are useful tools to identify risk groups and conduct prediction by employing binary recursive partitio
Phase Ⅰ designs traditionally use the dose-limiting toxicity(DLT),a binary endpoint based on the first treatment cycle,to locate the maximum-tolerated dose(MTD)assuming a monotonic relationship betwee
Computer simulations based on computational fluid dynamics,finite element analysis,discrete element models and multi-physics codes are widely used in automotive,finance,data centers and many other app
Much attention has been paid to estimating the causal effect of adherence to a randomized protocol using instrumental variables to adjust for unmeasured confounding.
To monitor the incidence rates of cancers,AIDS,cardiovascular diseases,and other chronicle or infectious diseases,some global,national and regional reporting systems have been built to collect/provide
Compositional data arise naturally in many practical problems and the analysis of such data presents many statistical challenges,especially in high dimensions.
Todays data pose unprecedented challenges to statisticians.It may be incomplete,corrupted or exposed to some unknown source of contamination.
Copy number alteration(CNA)data have been collected to study disease related chro-mosomal amplifications and deletions.
Efficacious and effective interventions usually involve multiple factors.And it is fantasy to have pilot information about the main effects and their interactions for all factors at the time of initia
Subgroup analysis becomes a popular practice in analyzing clinical trial data for identifying subgroup of patients who may benefit from the treatment of interest.