  We propose a vector generalized additive modeling framework for taking into
account the effect of covariates on angular density functions in a multivariate
extreme value context. The proposed methods are tailored for settings where the
dependence between extreme values may change according to covariates. We devise
a maximum penalized log-likelihood estimator, discuss details of the estimation
procedure, and derive its consistency and asymptotic normality. The simulation
study suggests that the proposed methods perform well in a wealth of simulation
scenarios by accurately recovering the true covariate-adjusted angular density.
Our empirical analysis reveals relevant dynamics of the dependence between
extreme air temperatures in two alpine resorts during the winter season.
Supplementary materials for this article are available online.
