一、报告题目

Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator

二、报告人

陈晓林 曲阜师范大学统计学院

三、报告时间

2018年11月1日（周四） 16:15-17:00

四、报告地点

知新楼B219

五、报告人简介

陈晓林，2012年于中国科学院数学与系统科学研究院获博士学位，现为曲阜师范大学统计学院副教授。研究方向主要为生存分析、高维数据分析、缺失数据的统计推断等。迄今，已在Computational Statistics and Data Analysis、Annals of the Institute of Statistical Mathematics、Lifetime Data Analysis等国际统计学期刊发表学术论文15篇，主持和参与国家自然科学基金、国家社会科学基金、山东省自然科学基金10余项。

六、 报告摘要

Due to the coexistence of ultra-high dimensionality and right censoring, it is very challenging to develop feature screening procedure for ultra-high-dimensional survival data. In this talk, I will present a joint screening approach for the sparse additive hazards model with ultra-high-dimensional features. This method is based on a sparsity-restricted pseudo-score estimator which could be obtained effectively through the iterative hard-thresholding algorithm. My coauthors and I have established the sure screening property of the proposed procedure theoretically under rather mild assumptions. Extensive simulation studies verify its improvements over the main existing screening approaches for ultra-high-dimensional survival data. Finally, the proposed screening method is illustrated by dataset from a breast cancer study.

七、主办单位

山东大学经济学院