走向现代数学学术报告 - 黄国文助理教授（No. 901)

报告题目：Missing Data Imputation Strategies: A Comparative Analysis of hourly and daily pollution Models

报告时间：2025年12月25日 10:00

腾讯会议ID：925324773

报 告 人：黄国文 助理教授（加拿大西安大略大学）

邀 请 人：单丽 副教授

报告摘要：Epidemiological studies have consistently demonstrated the association between daily pollution exposure and disease outcomes. To estimate daily exposure, hourly pollution data are commonly aggregated, but missing data pose a significant challenge to this approach. To overcome this issue, some researchers have developed various models to impute missing hourly data. Alternatively, directly modelling pollution exposure on a daily basis is possible, thereby avoiding the computational burden of hourly pollution modelling. However, the performance of these two modelling strategies remains unclear. This study conducts a comparative assessment between hourly and daily modelling strategies for the purpose of estimating daily pollution exposure. Utilizing data derived from Guangzhou city, the analysis encompasses diverse scenarios of data absence. The outcomes consistently highlight the superior performance of daily pollution models in terms of mitigated bias and diminished RMSE values.

报告人简介：黄国文博士在加拿大西安大略大学（Western University）统计学系任教。他于英国格拉斯哥大学获得统计学博士学位，曾在台湾清华大学和多伦多大学从事博士后研究工作。加入西安大略大学之前，黄博士曾任汕头大学副教授。他的研究方向主要集中于空间统计学，尤其致力于空气污染数据的统计建模及其对人类健康影响的研究。