Regression‐type models for extremal dependence

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 covari...

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Bibliographic Details
Published in:	Scandinavian journal of statistics Vol. 46; no. 4; pp. 1141 - 1167
Main Authors:	Mhalla, L., Carvalho, M., Chavez‐Demoulin, V.
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01-12-2019
Subjects:	Air temperature angular density Asymptotic methods Computer simulation covariate adjustment Density Dependence Economic models Empirical analysis Extreme values Normality penalized log‐likelihood Regression analysis statistics of multivariate extremes VGAM
Online Access:	Get full text
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Summary:	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.
ISSN:	0303-6898 1467-9469
DOI:	10.1111/sjos.12388