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  • 统计与管理学院2017年学术报告第53

     

    【主  题】Strong Independence Screening in Survival analysis

    【报告人】王学钦

    中山大学数学学院和中山医学院双聘教授,入选教育部新世纪优秀人才支持计划,获国家自然科学基金优秀青年基金

    【时  间】 2017年8月18日(星期五)14:30-15:15

    【地  点】 上海财经大学统计与管理学院大楼1316室

    【摘 要】Ranking by marginal utility provides an efficient way to reduce the data from ultra-high dimension to portable size. In order to handle the complex big data in great variability, the statistic that can measure the nonlinear relationship between response and marginal predictor were extensively discussed recently. Comparing to the regression analysis, it is more challenging when the response is the survival time with possible censoring. We propose a novel method to measure the marginal dependency between survival time and predictors. A screening criteria is presented to determine an active set to include important predictors and exclude unimportant predictors. It is shown that the proposed procedure enjoys good statistical properties. Its performance in finite sample size is evaluated via simulations and illustrated by a real data analysis.