学术报告（王友乾，广东财经大学四十周年校庆学术年学术活动-珠韵论坛第六期）
报告题目：异方差模型的稳健回归
报告人：王友乾 教授
时间：2023年12月21日(周四)下午3:00
地点：腾讯会议 391210501
报告提纲:英文摘要：
Standard methods for forecasting electricity loads are not robust to cyberattacks on electricity demand data, potentially leading to severe consequences such as major economic loss or a system blackout. Methods are required that can handle forecasting under these conditions and detect outliers that would otherwise go unnoticed. The key challenge is to remove as many outliers as possible while maintaining enough clean data to use in the regression. In this paper we investigate robust approaches with datadriven tuning parameters, and in particular present an adaptive trimmed regression method that can better detect outliers and provide improved forecasts. In general, datadriven approaches perform much better than their fixed tuning parameter counterparts.Recommendations for future work are provided.
王友乾，1991年在英国牛津大学获得统计学博士学位，历任美国哈佛大学副教授、新加坡国立大学副教授、澳大利亚联邦科学院首席科学家、澳大利亚昆士兰大学应用统计数学首席教授等。王教授在国际学术期刊上发表科技论文两百余篇（包括biometrika，biometrics，jasa，annals of statistics等顶级统计期刊），论文引用超过两千余次。