145 citations · 168 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Anchored Geodesic Analysis for Multivariate Extremes
Alberto Quaini, Chen Zhou
Extremal dependence is naturally described by the angular law of large multivariate observations. We introduce anchored geodesic component analysis (AGCA), a dimension-reduction me…
Graphical lasso for extremes
Phyllis Wan, Chen Zhou
In this paper, we estimate the sparse dependence structure in the tail region of a multivariate random vector, potentially of high dimension. The tail dependence is modeled via a g…
Estimating probabilities of multivariate failure sets based on pairwise tail dependence coefficients
Anna Kiriliouk, Chen Zhou
Estimating probabilities of extreme events involving multiple risk factors is a critical challenge in fields such as finance and climate science. This paper proposes a parametric a…
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…