2017年06月13日，星期二上午10:30在大学城广州大学文清楼107。中山大学王学钦教授为各位学子带来了精彩的学术讲座。

 

王学钦教授，中山大学数学学院和中山医学院双聘教授，博士生导师；中山大学统计学科带头人；中山大学华南统计科学研究中心执行主任；国家优秀青年基金获得者，教育部新世纪人才，教育部统计专业教指委委员；研究领域为非参多元统计学、统计学习、和精准医学。

 

报告摘要

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.