Modeling inter-dependence among multiple risks often faces statistical as well as modeling challenges, with considerable uncertainty arising naturally. This issue is crucial in modern risk management and regulation regimes in banking and insurance. To deal with the uncertainty at the level of dependence in multivariate models, various techniques in robust risk aggregation have been developed over the past decade. In this talk, I will briefly review some recent developments and open challenges on this topic. We will then focus on the problem of merging p-values in multiple hypothesis testing, a classic problem in statistical theory. It turns out that recent results in robust risk aggregation become useful for designing conservative and precise averaging methods of p-values. If time permits, I will also discuss the concept of e-values as well as the problem of robust merging methods for e-values. This talk is based on joint work with Vladimir Vovk.
Dr. Ruodu Wang is University Research Chair and Associate Professor of Actuarial Science at the University of Waterloo in Canada. He received his PhD in Mathematics (2012) from the Georgia Institute of Technology, after completing his Bachelor (2006) and Master's (2009) degrees at Peking University. He holds editorial positions of leading academic journals in Actuarial Science, including Co-Editor of the European Actuarial Journal, and Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association. He is an affiliated member of RiskLab at ETH Zurich. He received one of Canada's 125 Discovery Accelerator Supplement Awards from the Natural Sciences and Engineering Research Council of Canada in 2018.