An M-estimator of spatial tail dependence

John H.J. Einmahl, Anna Kiriliouk, Andrea Krajina, Johan Segers

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max-stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.

langue originaleAnglais
Pages (de - à)275-298
Nombre de pages24
journalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume78
Numéro de publication1
Les DOIs
étatPublié - 1 janv. 2016
Modification externeOui

Empreinte digitale

Tail Dependence
Spatial Dependence
Stable Laws
M-estimator
Stable Process
Empirical Process
Asymptotic Variance
Wind Speed
Additive Noise
High-dimensional Data
Data-driven
Margin
Simulation Experiment
Minimise
Estimator
Model
Observation
Simulation experiment
Empirical process
The Netherlands

mots-clés

    Citer ceci

    Einmahl, John H.J. ; Kiriliouk, Anna ; Krajina, Andrea ; Segers, Johan. / An M-estimator of spatial tail dependence. Dans: Journal of the Royal Statistical Society. Series B: Statistical Methodology. 2016 ; Vol 78, Numéro 1. p. 275-298.
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    abstract = "Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max-stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.",
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    An M-estimator of spatial tail dependence. / Einmahl, John H.J.; Kiriliouk, Anna; Krajina, Andrea; Segers, Johan.

    Dans: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol 78, Numéro 1, 01.01.2016, p. 275-298.

    Résultats de recherche: Contribution à un journal/une revueArticle

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    AU - Einmahl, John H.J.

    AU - Kiriliouk, Anna

    AU - Krajina, Andrea

    AU - Segers, Johan

    PY - 2016/1/1

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    N2 - Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max-stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.

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    KW - Brown–Resnick process

    KW - Exceedances

    KW - Multivariate extremes

    KW - Ranks

    KW - Spatial statistics

    KW - Stable tail dependence function

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    JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology

    JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology

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