报 告 人： 李润泽
报告地点： 数学与统计学院四楼报告厅
报告时间： 2016年10月31日星期一10:00-11:00
报告简介：Error variance estimation plays an important role in statistical inference for high dimensional regression models. This presentation concerns with error variance estimation in high dimensional sparse models. This talk will start with 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. This talk further presents an accurate estimate for error variance inultrahigh dimensional sparse model by effectively integrating sure independence screening and refitted cross-validation techniques. The root n consistency and the asymptotic normality of the resulting estimate are established. Monte Carlo simulation study are conducted 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：
Verne M. Willaman Professor of Statistics, Penn State University NSF Career Award, 2004 Fellow, Institute of Mathematical Statistics Fellow, American Statistical Association The United Nations' World Meteorological Organization Gerbier-Mumm International Award for 2012 (Selection criterion for this award) Editor of The Annals of Statistics (2013 - 2015) Highly Cited Researcher in Mathematics, 2014 & 2015
