Spatial risk measures for max-stable and max-mixture processes

Ahmed, Manaf; Maume-Deschamps, Véronique; Ribereau, Pierre; Vial, Céline

doi:10.1080/17442508.2019.1687703

Cited by 3 publications

(1 citation statement)

References 29 publications

(35 reference statements)

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“…}, which may be exploited for pairwise likelihood inference, usually based on high threshold exceedances by censoring low values. This model has been used, e.g., by Bacro et al (2016) and Ahmed et al (2017Ahmed et al ( , 2019, but it has the drawback of being usually quite heavily parametrized and that estimation of the crucial parameter a is difficult. In the next section, we present more parsimonious spatial extreme-value models that can also capture both asymptotic dependence regimes.…”

Section: Asymptotic Dependence Classesmentioning

confidence: 99%

Advances in Statistical Modeling of Spatial Extremes

Huser¹,

Wadsworth²

2020

Preprint

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The classical modeling of spatial extremes relies on asymptotic models (i.e., max-stable processes or r-Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at finite levels, empirical evidence often suggests that such asymptotic models are too rigidly constrained, and that they do not adequately capture the frequent situation where more severe events tend to be spatially more localized. In other words, these asymptotic models have a strong tail dependence that persists at increasingly high levels, while data usually suggest that it should weaken instead. Another well-known limitation of classical spatial extremes models is that they are either computationally prohibitive to fit in high dimensions, or they need to be fitted using less efficient techniques. In this review paper, we describe recent progress in the modeling and inference for spatial extremes, focusing on new models that have more flexible tail structures that can bridge asymptotic dependence classes, and that are more easily amenable to likelihood-based inference for large datasets. In particular, we discuss various types of random scale constructions, as well as the conditional spatial extremes model, which have recently been getting increasing attention within the statistics of extremes community. We illustrate some of these new spatial models on two different environmental applications.

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Section: Asymptotic Dependence Classesmentioning

confidence: 99%

Advances in Statistical Modeling of Spatial Extremes

Huser¹,

Wadsworth²

2020

Preprint

View full text Add to dashboard Cite

show abstract

Advances in statistical modeling of spatial extremes

Huser

Wadsworth

2020

WIREs Computational Stats

View full text Add to dashboard Cite

The classical modeling of spatial extremes relies on asymptotic models (i.e., max-stable or r-Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at finite levels, empirical evidence often suggests that such asymptotic models are too rigidly constrained, and that they do not adequately capture the frequent situation where more severe events tend to be spatially more localized. In other words, these asymptotic models have a strong tail dependence that persists at increasingly high levels, while data usually suggest that it should weaken instead. Another well-known limitation of classical spatial extremes models is that they are either computationally prohibitive to fit in high dimensions, or they need to be fitted using less efficient techniques. In this review paper, we describe recent progress in the modeling and inference for spatial extremes, focusing on new models that have more flexible tail structures that can bridge asymptotic dependence classes, and that are more easily amenable to likelihood-based inference for large datasets. In particular, we discuss various types of random scale constructions, as well as the conditional spatial extremes model, which have recently been getting increasing attention within the statistics of extremes community. We illustrate some of these new spatial models on two different environmental applications. This article is categorized under: Data: Types and Structure > Image and Spatial Data Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods K E Y W O R D S asymptotic dependence and independence, extreme-value theory, max-stable process, Pareto process, random scale mixture

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