报告题目：Model-Free Feature Screening for High-Dimensional Survival Data。

报告摘要：With the rapid-growth-in-size scientific data in various disciplines, feature screening plays an important role to reduce the high-dimensionality to a moderate scale in many scientific fields. In this talk, we introduce a unified and robust model-free feature screening approach for high-dimensional survival data with censoring, which has several advantages: it is a model-free approach under a general model framework, and hence avoids the complication to specify an actual model form with huge number of candidate variables; under mild conditions without requiring the existence of any moment of the response, it enjoys the ranking consistency and sure screening properties in ultra-high dimension. In particular, we impose a conditional independence assumption of the response and the censoring variable given each covariate, instead of assuming the censoring variable is independent of the response and the covariates. Moreover, we also propose a more robust variant to the new procedure, which possesses desirable theoretical properties without any finite moment condition of the predictors and the response. The computation of the newly proposed methods does not require any complicated numerical optimization and it is fast and easy to implement. Extensive numerical studies demonstrate that the proposed methods perform competitively for various configurations. Application is illustrated with an analysis of a genetic data set.

主讲人：刘显慧

报告时间：2018年12月28日 15:00

报告地点：精工楼7-104

个人简介：刘显慧博士，江西财经大学统计学院讲师，2009年在中国科技大学就读博士，2012年拿到统计学博士学位，2012年至今在江西财经大学统计学院工作，曾多次在香港中文大学统计系做访问学者，研究方向为高维统计分析。