Por Miguel de Carvalho (University of Edinburgh - UK).
In this talk I will discuss a vector generalised additive modelling 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.
(Joint work with L. Mhalla and V. Chavez-Demoulin).
[ver também: Seminário "Modelling Spatial Extreme Events" > coffe break entre as 15h30 e as 16h00]
