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美国密歇根大学李颐教授:Simultaneous estimation and inference for high-dimensional linear models

发布时间:2018-10-24


主题:Simultaneous estimation and inference for high-dimensional linear models

主讲人:美国密歇根大学李颐教授

主持人:统计学院 林华珍教授

时间:2018年10月30日(星期二)下午4:00-5:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处


主讲人简介:

李颐,美国密歇根大学博士,哈佛大学博士后,现任美国密歇根大学教授,密歇根大学公共卫生学院中国项目主任,2011-2016年为Kidney Epidemiology and Cost Center主任。他的研究兴趣包括:生存分析、流行病学方法、高维数据统计方法、癌症试验和/或观察性研究的统计方法、测量误差问题、随机效应模型、临床试验的适应性设计等。他是包括统计学顶级期刊Journal of the American Statistical Association、Biometrics、Scandinavian Journal of Statistics在内的六个期刊副主编。他已发表论文近170篇,其中在国际顶级杂志Journal of the American Statistical Association、Biometrics、Biometrika、Journal of the Royal Statistical Society:Series B等期刊发表论文近30篇。

详情请见个人主页:http://www-personal.umich.edu/~yili/

主要内容:

Drawing inferences for high-dimensional models is challenging as regular asymptotic theories are not applicable. This talk proposes a new framework of simultaneous estimation and inference for high-dimensional linear models. By smoothing over partial regression estimates based on a given variable selection scheme, we reduce the problem to a low-dimensional least squares estimation. The procedure, termed as Selection-assisted Partial Regression and Smoothing (SPARES), utilizes data splitting along with variable selection and partial regression. We show that the SPARES estimator is asymptotically unbiased and normal, and derive its variance via a nonparametric delta method. The utility of the procedure is evaluated under various simulation scenarios and via comparisons with the de-biased LASSO estimators, a major competitor. We apply the method to analyze two genomic datasets and obtain biologically meaningful results.