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统计学院系列学术报告(三)
发布日期:2018-06-26 浏览次数:

报告题目:Large matrix estimation for time series data

报告人:南宾

报告摘要Motivated by the Markowitz optimal portfolio theory and the applications in other scientific fields, we consider the estimation of large covariance and precision matrices from high-dimensional observations with slowly decaying temporal dependence that is bounded by certain polynomial decay rate. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. The rates of convergence are obtained, and a gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations.

报告时间7月1日上午10:00-10:30

报告地点科技楼二楼北会议室

报告人简介南宾,密歇根大学统计系与生物统计系教授。美国统计学会Fellow,国际数理统计学会Fellow。主要研究兴趣为生物医学统计,包括生存分析,高维脑图像数据分析,带变点的纵向数据分析等。个人主页:https://sph.umich.edu/faculty-profiles/nan-bin.html

 


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