145 citations · 168 across the 13 of their papers we have counts for
14 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…
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…
Trends in tail dependence of heteroscedastic extremes
John H. J. Einmahl, Chen Zhou
We consider multivariate extreme value statistics for independent but nonidentically distributed random vectors. In particular, the data may have varying tail copulas and also hete…
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…
Tail inference using extreme U-statistics
Jochem Oorschot, Johan Segers, Chen Zhou
Extreme U-statistics arise when the kernel of a U-statistic has a high degree but depends only on its arguments through a small number of top order statistics. As the kernel degree…