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Robust Nonparametric Independence Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Features
澳门新葡8455最新网站:2017年05月31日 09:05 点击数:

报告人:周勇

报告地点:澳门新葡8455最新网站四楼报告厅

报告澳门新葡8455最新网站:2017年06月01日星期四10:00-11:00

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报告摘要:

In this article, we propose a marginal robust independence screening procedure based on the entire conditional distribution function of the response. The new method is model-free which dose not require specification of models, and can perform well in the presence of the heavy tails and extreme values in the response. We further show that the proposed method possesses important theoretical properties including ranking consistency property and sure screening property. To reduce the false selection rate, model selection consistency is also be achieved theoretically. We conduct Monte Carlo simulation studies to demonstrate that the proposed methodology exhibits more competitive performance than the existing feature screening methods under different model settings and illustrate the proposed method through a real data analysis.

 

主讲人概况:

周勇,国家杰出青年基金获得者,教育部长江学者特聘教授,中国科新葡京最新官网百人计划入选者,国务院政府特殊津贴专家,“新世纪百千万人才工程”国家级人选。1994年获中国科新葡京最新官网应用数学所博士学位。中国科新葡京最新官网数学与系统科学研究院研究员,上海财经大学统计与管理新葡京最新官网院长。周勇教授主要从事大数据分析与建模、金融计量、风险管理、计量经济学、统计理论和方法等科学研究工作,取得许多有重要学术价值和影响的研究成果。先后承担并完成国家自然科学基金项目,国家杰出青年基金,自然科学基金委重点项目等科学项目10余项,曾获得省部级奖励二项。在包括国际顶级统计杂志《The Annals of Statistics》、《Journal of The American Statistical Association》,《Biometrika》,《Journal of the Royal statistical Society--Theory and Method》和国际顶级的计量经济学杂志《Journal of Econometrics》和《Journal of Business & Economic Statistics》等学术期刊上发表学术论文100余篇,其中,SCI/SSCI索引论文近100篇。

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