<li id="ylugo"><span id="ylugo"><source id="ylugo"></source></span></li>

  • <code id="ylugo"><samp id="ylugo"><i id="ylugo"></i></samp></code><meter id="ylugo"><nav id="ylugo"></nav></meter>
    1. 【95周年校慶系列講座】Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective

      時間:2020-07-23         閱讀:

      光華講壇——社會名流與企業家論壇第 5762 期

      (線上講座)

      主題Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective

      主講人伊利諾伊大學香檳分校 邵曉峰教授

      主持人統計學院 常晉源教授

      時間2020年7月24日(周五)9:30-10:30

      直播平臺及會議IDZoom,會議ID:921 1079 7812

      主辦單位:統計研究中心 數據科學與商業智能聯合實驗室 統計學院 科研處

      主講人簡介:

      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.

      邵曉峰,美國伊利諾伊大學香檳分校統計學教授,博士生項目主任。他于2006年獲得了芝加哥大學的統計學博士學位,此后一直在美國伊利諾伊大學香檳分校任教。主要研究方向為時間序列分析、高維數據分析、函數型數據分析、變點分析、重采樣方法和漸進理論。他是當選的ASA和IMS成員。

      內容提要:

      I will present our recent work to model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S. Joint work with Feiyu Jiang and Zifeng Zhao.

      本報告將介紹我們最近的工作——通過分段線性趨勢模型對COVID-19累計確診病例和死亡人數(以對數為尺度)的軌跡進行建模。該模型能通過變點自然地捕捉傳染病增長率的相變,并且由于其半參數性而具有很強的可解釋性。在方法層面,本報告采用新興自歸化(SN)技術(Shao, 2010)來檢驗和估計非平穩時間序列線性趨勢中的單一變點。進一步將基于SN的變點測試與NOT算法(Baranowski et al., 2019)相結合來實現多變點估計。利用所提出的方法,本報告分析了30個主要國家COVID-19累計病例和死亡人數的軌跡,發現了一些有趣的現象可能對不同國家應對疫情的有效性產生相關影響。此外,基于變點檢測算法和可擴展的外推函數,本報告對COVID-19設計了一個簡單的兩階段預測方案,并在預測美國累計死亡人數方面能有很好的表現。本研究是與Feiyu Jiang和Zifeng Zhao一起合作完成的。

      亚洲彩票