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Change-point detection for COVID-19 time series via self-normalization

时间:2021-03-04         阅读:

光华讲坛——社会名流与企业家论坛第5630期


主题Change-point detection for COVID-19 time series via self-normalization

主讲人伊利诺伊大学香槟分校 邵晓峰教授

主持人统计学院 常晋源教授

时间2021年3月5日(周五)上午9:30-10:30

直播平台及会议ID:腾讯会议,931 851 743

主办单位:数据科学与商业智能联合实验室 统计学院 科研处

主讲人简介:

Dr. Shao is Professor of Statistics and PhD program director, at the Department of Statistics, University of Illinois at Urbana-Champaign (UIUC). He received his PhD in Statistics from University of Chicago in 2006 and has been on the UIUC faculty since then. Dr. Shao's research interests include time series analysis, high-dimensional data analysis, functional data analysis, change-point analysis, resampling methods and asymptotic theory. He is an elected ASA and IMS fellow.

邵晓峰,美国伊利诺伊大学香槟分校统计学教授,博士生项目主任。他于2006年获得了芝加哥大学的统计学博士学位,此后一直在美国伊利诺伊大学香槟分校任教。主要研究方向为时间序列分析、高维数据分析、函数型数据分析、变点分析、重采样方法和渐进理论。他是当选的ASA和IMS成员。

内容提要:

This talk consists of two parts. In the first part, I will review some basic idea of self-normalization (SN) for inference of time series in the context of confidence interval construction and change-point testing in mean. In the second part, I will present a piecewise linear quantile trend model to model infection trajectories of COVID-19 daily new cases. To estimate the change-points in the linear trend, we develop a new segmentation algorithm based on SN test statistics and local scanning. Data analysis for COVID-19 infection trends in many countries demonstrates the usefulness of our new model and segmentation method.

本次报告由两部分组成。第一部分,将回顾在时间序列的统计推断中,归一化思想在置信区间构造和均值变点的检验中的应用。在第二部分,将提出分段线性分位数趋势模型,以模拟COVID-19每日新增病例的感染轨迹。为了估计线性趋势中的变化点,本文提出了一种新的基于SN检验统计量和局部扫描的分割算法。通过对多个国家COVID-19感染趋势的数据分析发现,本文所提的新模型和分割方法是实用的。

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