9 papers
Generalized Linear Models for Extremes: Estimation and Inference in High Dimensions
Liujun Chen, Chen Zhou
We propose a regression model for the extreme tail of a response variable, in which covariates rescale the tail without changing its shape. A single covariate-dependent function th…
High Dimensional Mean Test for Shrinking Random Variables with Applications to Backtesting
Liujun Chen, Chen Zhou
We propose a high dimensional mean test framework for shrinking random variables, where the underlying random variables shrink to zero as the sample size increases. By pooling obse…
Clustering Tails in High Dimension
Liujun Chen, Marco Oesting, Chen Zhou
One potential solution to combat the scarcity of tail observations in extreme value analysis is to integrate information from multiple datasets sharing similar tail properties, for…
Max-Linear Tail Regression
Liujun Chen, Deyuan Li, Zhengjun Zhang
The relationship between a response variable and its covariates can vary significantly, especially in scenarios where covariates take on extremely high or low values. This paper in…
High dimensional inference for extreme value indices
Liujun Chen, Chen Zhou
When applying multivariate extreme value statistics to analyze tail risk in compound events defined by a multivariate random vector, one often assumes that all dimensions share the…
Tail Gini Functional under Asymptotic Independence
Zhaowen Wang, Liujun Chen, Deyuan Li
Tail Gini functional is a measure of tail risk variability for systemic risks, and has many applications in banking, finance and insurance. Meanwhile, there is growing attention on…
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…
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…