73 citations · 73 across the 6 of their papers we have counts for
6 papers
Adapting the Hill estimator to distributed inference: dealing with the bias
Liujun Chen, Deyuan Li, Chen Zhou
The distributed Hill estimator is a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. In applications, estimates based…
Distributed Inference for Tail Risk
Liujun Chen, Deyuan Li, Chen Zhou
For measuring tail risk with scarce extreme events, extreme value analysis is often invoked as the statistical tool to extrapolate to the tail of a distribution. The presence of la…
Modeling and Decoupling Systemic Risk
Jingyu Ji, Deyuan Li, Zhengjun Zhang
Identifying systemic risk patterns in geopolitical, economic, financial, environmental, transportation, epidemiological systems and their impacts is the key to risk management. Thi…
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers
Nathan Tsoi, Kate Candon, Deyuan Li +2
While neural network binary classifiers are often evaluated on metrics such as Accuracy and -Score, they are commonly trained with a cross-entropy objective. How can this trai…
Estimating Extreme Value Index by Subsampling for Massive Datasets with Heavy-Tailed Distributions
Yongxin Li, Liujun Chen, Deyuan Li +1
Modern statistical analyses often encounter datasets with massive sizes and heavy-tailed distributions. For datasets with massive sizes, traditional estimation methods can hardly b…
Weighted approximations of tail copula processes with application to testing the bivariate extreme value condition
John H. J. Einmahl, Laurens de Haan, Deyuan Li
Consider i.i.d. random vectors on , with unknown, common distribution function . Under a sharpening of the extreme value condition on , we derive a weighted…