新闻中心

News

创业导向 - 独具特色 - 世界一流
学术活动

Integrated Powered Density: Screening Ultrahigh-Dimensional Covariates with Survival Outcomes

作者:西南财经大学    发布:2017-06-15   

ProfessorYi Li, University of MichiganIntegrated Powered Density: Screening Ultrahigh-Dimensional Covariates with Survival Outcomes


  题:Integrated Powered Density: Screening Ultrahigh-Dimensional Covariates with Survival Outcomes

 

主讲人:Professor Yi Li

主持人:林华珍教授

  间:20170616日(星期五)下午2:00-3:00

  点:弘远楼402B学术会议室

主办单位:统计研究中心  统计学院   科研处

 

主讲人简介:

李颐教授,美国Michigen大学博士,现就职Michigen大学,也是全美肾脏和费用成本中心的总监(director)。这个中心包括50多名来自统计系、卫生管理与政策系、手术系和肾脏系等的教授。李颐教授还是哈佛大学的兼职教授。李颐教授是观察研究以及基因研究方面的杰出代表人物。他在生存分析、基因组统计研究、观察研究、高维数据分析、测量误差问题、空间数据分析、随机效应模型、临床试验的适定性设计以及变量选择等众多领域都有突出的贡献。他在统计学顶级杂志(JASA, Biometrika, JRSSB, Biometrics等)上发表有140多篇文章,还担任JASA, Scandinavian Journal of Statistics, Biometrics等很多知名统计杂志的副主编及评审。

详情请见个人主页:http://www-personal.umich.edu/~yili/

 

内容提要:

Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data for the purpose of identifying predictive biomarkers, understanding biological mechanisms and making parsimonious predictions.To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. This proposed new class of statistics, with a flexible weighting scheme, is general and includes the Kolmogorov statistic as a special case. Moreover, the method does not rely on rigid regression model assumptions and can be easily implemented. We show that our method possesses sure screening properties, and confirm the utility of the proposal with extensive simulation studies. We apply the method to analyze a multiple myeloma study on detecting gene signatures for cancer patients survival.