主题：Error Variance Estimation in High Dimensional Regression Models
主讲人：Runze Li, Penn State University
时间： 2017年10月26日 下午3：00
地点：25教学楼C区401
讲座内容：Error variance estimation plays an important role in statistical inference for high dimensional regression models. This paper concerns with error variance estimation in high dimensional sparse additive model. We study the asymptotic behavior of the traditional mean squared errors, the naive estimate of error variance, and show that it may significantly underestimate the error variance due to spurious correlations which are even higher in nonparametric models than linear models. We further propose an accurate estimate for error variance in ultrahigh dimensional sparse additive model by effectively integrating sure independence screening and refitted cross-validation techniques (Fan, Guo and Hao, 2012). The root $n$ consistency and the asymptotic normality of the resulting estimate are established. We conduct Monte Carlo simulation study to examine the finite sample performance of the newly proposed estimate. A real data example is used to illustrate the proposed methodology.
主讲人简介：李润泽现任美国宾州州立大学统计系冠名讲座教授。中国国家教育部短期长江讲座教授，世界顶级统计学刊物The Annals of Statistics的联合主编。2014年被评为99名高被引（亦被称为世界顶尖）数学家之一（Highly Cited Researchers 2014 in Mathematics）。
李润泽于2000年从美国北卡大学主校区毕业获博士学位，之后一直在宾州州立大学统计系工作。
2008年即升任正教授。
2012年被选为讲座教授（Distinguished Professor）。
2014年被任命为冠名讲座教授(Verne M Willaman Professor)。
2015年被选为中国教育部长江讲座教授。
他曾担任Statistica Sinica和The Annals of Statistics的副主编，现任两个世界顶级统计学刊物The Annals of Statistics的主编和Journal of American Statistical Association 的副主编。
国际最权威统计学学会Institute of Mathematical Statistics和American Statistical Association资深会士。
