题目：Change-point analysis for modern data

主讲人：加州大学戴维斯分校 陈豪副教授

主持人：西南财经大学统计学院 常晋源教授

时间：2020年6月12日（星期五）10:30-11:50

直播平台及会议ID：腾讯会议，744 746 640

报告摘要：

After observing snapshots of a network, can we tell if there has been a change in dynamics? After collecting spiking activities of thousands of neurons in the brain, how shall we extract meaningful information from the recording?  We introduce a change-point analysis framework utilizing graphs representing the similarity among observations.  This approach is non-parametric and can be applied to data when an informative similarity measure can be defined.  Analytic approximations to the significance of the test statistics are derived to make the method fast applicable to long sequences.  The method is illustrated through the analysis of the Neuropixels data.

主讲人简介：

Hao Chen is an Associate Professor of Statistics at University of California, Davis.  Her current research interest lies in developing practical tools for understanding high-dimensional data and non-Euclidean data. She is the recipient of an NSF CAREER award.