Multivariate generalized Pareto distributions: Parametrizations, representations, and properties

Mathematics and Statistics

Associated organisational unit

Extreme Value Theory

Electronic data

12MJMVA_2017_214_12-07
Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 165, 2018 DOI: 10.1016/j.jmva.2017.12.003
Accepted author manuscript, 199 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Keywords

Exceedances, Maxima, Stable tail dependence function, Tail copula, Linear combination

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Multivariate generalized Pareto distributions: Parametrizations, representations, and properties. / Rootzen, Holger; Segers, Johan; Wadsworth, Jennifer Lynne.
In: Journal of Multivariate Analysis, Vol. 165, 05.2018, p. 117-131.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{e0dcfd8e47ab4daa95946497918b1f9d,

title = "Multivariate generalized Pareto distributions: Parametrizations, representations, and properties",

abstract = "Multivariate generalized Pareto distributions arise as the limit distributions of exceedances over multivariate thresholds of random vectors in the domain of attraction of a max-stable distribution. These distributions can be parametrized and represented in a number of different ways. Moreover, generalized Pareto distributions enjoy a number of interesting stability properties. An overview of the main features of such distributions is given, expressed compactly in severalparametrizations, giving the potential user of these distributions a convenient catalogue of ways to handle and work with generalized Pareto distributions.",

keywords = "Exceedances, Maxima, Stable tail dependence function, Tail copula, Linear combination",

author = "Holger Rootzen and Johan Segers and Wadsworth, {Jennifer Lynne}",

note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 165, 2018 DOI: 10.1016/j.jmva.2017.12.003",

year = "2018",

month = may,

language = "English",

volume = "165",

pages = "117--131",

journal = "Journal of Multivariate Analysis",

issn = "0047-259X",

publisher = "Academic Press Inc.",

}

RIS

TY - JOUR

T1 - Multivariate generalized Pareto distributions: Parametrizations, representations, and properties

AU - Rootzen, Holger

AU - Segers, Johan

AU - Wadsworth, Jennifer Lynne

N1 - This is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 165, 2018 DOI: 10.1016/j.jmva.2017.12.003

PY - 2018/5

Y1 - 2018/5

N2 - Multivariate generalized Pareto distributions arise as the limit distributions of exceedances over multivariate thresholds of random vectors in the domain of attraction of a max-stable distribution. These distributions can be parametrized and represented in a number of different ways. Moreover, generalized Pareto distributions enjoy a number of interesting stability properties. An overview of the main features of such distributions is given, expressed compactly in severalparametrizations, giving the potential user of these distributions a convenient catalogue of ways to handle and work with generalized Pareto distributions.

AB - Multivariate generalized Pareto distributions arise as the limit distributions of exceedances over multivariate thresholds of random vectors in the domain of attraction of a max-stable distribution. These distributions can be parametrized and represented in a number of different ways. Moreover, generalized Pareto distributions enjoy a number of interesting stability properties. An overview of the main features of such distributions is given, expressed compactly in severalparametrizations, giving the potential user of these distributions a convenient catalogue of ways to handle and work with generalized Pareto distributions.

KW - Exceedances

KW - Maxima

KW - Stable tail dependence function

KW - Tail copula

KW - Linear combination

M3 - Journal article

VL - 165

SP - 117

EP - 131

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

SN - 0047-259X

ER -

Research

Associated organisational unit

Electronic data

Keywords