博彩平台-正规博彩平台 学术讲座——Dr. Erick (Zhaolin) Li
发布者:殳妮 发布时间:2024-03-30 浏览次数:115
讲座时间:2024年4月1日 上午9:30
讲座地点:东校区财科馆317会议室
讲座题目Title: Two New Semi-Parametric Bounds and Their Applications
摘要Abstract: Many business processes are based on accumulated random realizations which have an expected value and a measure of variability. We revisit the tail behaviour of such sums when individual realizations are independent and develop a simple new lower bound on the tail probability as well as upper bound on the expected linear loss. The distribution of the realizations is unrestricted other than the assumed mean and variance. Our sharp bounds significantly improve over the existing aggregation results, which often ignore independence, and open up abundant practical applications including improved pricing of bundles, more precise option pricing, more efficient insurance design, and better inventory management.
Note: This is a joint work with Professor Artem Prokhorov in University of Sydney.
主讲人简介:Dr. Erick (Zhaolin) Li received a Ph.D. in Business Administration from The Pennsylvania State University, a Master of Commerce in Accounting from The University of New South Wales, and a Bachelor of Engineering in Materials Science & Industrial Engineering from Shanghai Jiao Tong University. Dr. Li has been with The University of Sydney Business School since January 2009. Before moving to Sydney, he had worked in Ernst & Young LLP, Southern Arkansas University, and City University of Hong Kong. His research interests spans two areas of interest: supply chain management (SCM) and the interfaces between operations management (OM) and other areas. He has published research articles in Decision Sciences, Management Science, and Production & Operations Management. Dr. Li is among Asia’s top scholars in Operations Management, according to Babbar et al., (2017) that was published in the International Journal of Production Economics. Dr. Li is also among the top 25 Supply Chain Management (SCM) scholars of Asia, according to a subsequent study done by Babbar et al., (2018).