报告题目：Long-memory log-linear zero-inflated generalized Poisson autoregression for Covid-19 pandemic modeling
报告人：许晓菲 特聘副研究员 武汉大学
报告时间：2025年1月8日 15:00-16:00
报告地点：数学楼第二报告厅
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：This paper describes the dynamics of daily new cases arising from the Covid-19 pandemic using a long-range dependent model. A new long memory model, LFIGX (Log-linear zero-inflated generalized Poisson integer-valued Fractionally Integrated GARCH process with exogenous covariates), is proposed to account for count time series data with long-run dependent effect. It provides a novel unified framework for integer-valued processes with serial and long-range dependence (positive or negative), over-dispersion, zero-inflation, nonlinearity, and exogenous variables effects. We adopt an adaptive Bayesian Markov Chain Monte Carlo (MCMC) sampling scheme for parameter estimation. This new modeling is applied to the daily new confirmed cases of Covid-19 pandemic in six countries including Japan, Vietnam, Italy, the United Kingdom, Brazil, and the United States. The LFIGX model provides insightful interpretations on the impacts of policy index and temperature, and delivers good forecasting performance to the dynamics of daily new cases in different countries. This is a joint work with Yijiong Zhang, Yan Liu, Yuichi Goto, Masanobu Taniguchi, and Ying Chen.
报告人简介：许晓菲，武汉大学数学与统计学院特聘副研究员。2020年博士毕业于新加坡国立大学，2021年-2022年5月担任早稻田大学助理教授。主要研究方向为函数型时间序列分析，整值时间序列分析和不平稳复杂高维时间序列预测等。在JBES，AOAS，Statistica Sinica等国际学术期刊上发表论文数篇，主持国家自然科学基金青年项目1项，参与国家自然科学基金指南引导类原创探索计划项目1项。
