报告地点：行健楼学术活动室526
Abstract: Accurate estimation of the extreme value index (EVI) is central to the analysis of tail risks. Although substantial theoretical progress has been made, most existing estimators rely solely on tail observations from the variable of interest, which are often too limited or unstable to yield reliable inferences. We propose a novel framework, Individual Fusion Learning (iFusion), to enhance EVI estimation across multiple data sources. The core idea is to enhance estimation for a target variable by strategically leveraging the information from the tails of related sources, thereby improving efficiency while maintaining statistical validity. Under an independence assumption, we construct an iFusion estimator based on the Hill estimator and establish its consistency and asymptotic normality. We extend the method to accommodate dependent sources via an adjusted fusion strategy. Simulation studies and an application to ozone concentration data demonstrate the significantly improved performance of iFusion method, particularly in variance reduction and out-of-sample prediction, highlighting its immediate practical value for multi-source extreme value analysis.
