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Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes

Describing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue, we develop a novel statistical method that describes extremal dependence taking advantage of the inherent tree-based dependence structure of the max-stable nested logistic distribution, and which identifies possible clusters of extreme variables using reversible jump Markov chain Monte Carlo techniques. Parsimonious representations are achieved when clusters of extreme variables are found to be completely independent. Moreover, we significantly decrease the computational complexity of full likelihood inference by deriving a recursive formula for the likelihood function of the nested logistic model. The method’s performance is verified through extensive simulation experiments which also compare different likelihood procedures. The new methodology is used to investigate the dependence relationships between extreme concentrations of multiple pollutants in California and how these concentrations are related to extreme weather conditions. Overall, we show that our approach allows for the representation of complex extremal dependence structures and has valid applications in multivariate data analysis, such as air pollution monitoring, where it can guide policymaking. Supplementary materials for this article are available online.

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Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.29 No.1 March 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 174 - 190

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

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Subjek

REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO
AIR POLLUTION
EXTREME EVENT
FAST LIKELIHOOD INFERENCE
NESTED LOGISTIC MODEL

Info Detail Spesifik

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Pernyataan Tanggungjawab

Sabrina Vettori, Raphaël Huser, Johan Segers, Marc G. Genton

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