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Regression‐type models for extremal dependence
Author(s) -
Mhalla L.,
Carvalho M.,
ChavezDemoulin V.
Publication year - 2019
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12388
Subject(s) - covariate , mathematics , estimator , consistency (knowledge bases) , extreme value theory , econometrics , statistics , asymptotic distribution , context (archaeology) , multivariate statistics , strong consistency , normality , geography , geometry , archaeology
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.