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Eigenvalues of high-dimensional spatial-sign covariance matrices
奥门金沙:2020年08月11日 17:53 点击数:

报告人:王勤文

报告地点:Tencent会议

报告奥门金沙:2020年08月14日星期五14:00-15:00

邀请人:郑术蓉

报告摘要:

In this talk, we will investigate limiting spectral properties of high dimensional sample spatial-sign covariance matrices. The populations under study are general enough with possibly known or unknown location vectors, to include the popular independent components model and the family of elliptical distributions. The first result establishes that the empirical spectral distributions of high dimensional sample spatial-sign covariance matrices converge to a deterministic generalized Marcenko-Pastur law. The second result establishes the central limit theorems for a class of linear spectral statistics and we will show for data with known or unknown location vectors, the difference of their CLTs only lies in a mean shift.

会议ID:338 575 091

主讲人概况:

王勤文,青年副研究员,2015年获得浙江大学概率论与数理统计专业博士学位,之后在美国宾夕法尼亚大学从事博士后研究工作。主要研究方向为随机矩阵理论及其应用,高维统计推断。

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