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  • 统计与管理学院2017年学术报告第48

     

    【主  题】Error Variance Estimation in Ultrahigh Dimensional Additive Models

    【报告人】李润泽,教授

    宾夕法尼亚州立大学

    【时  间】 2017年07月03日(星期一)15:00-16:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】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.

    【邀请人】 刘旭