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(11.1)高级经济学讲座第226期
发布时间:2018-10-31 12:21:47    编辑:王永军    点击:[]

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:

2018111日 (周四) 下午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

 

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