Max‐Stable Processes

Padoan, Simone A.

doi:10.1002/9780470057339.vnn022

Cited by 3 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Asymptotically independent processes for modelling extreme values were discussed by de Haan & Zhou (2011), Wadsworth & Tawn (2012) and Padoan (2013a). Reviews of recent advances in the statistical modeling of spatial extremes are , Cooley et al (2012), Padoan (2013b), Ribatet (2013) and .…”

Section: Introductionmentioning

confidence: 99%

Multivariate max-stable spatial processes

Genton¹,

Padoan²,

Sang³

2015

Biometrika

View full text Add to dashboard Cite

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown-Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and of an application to daily maximum wind speed and wind gust.

show abstract

Section: Introductionmentioning

confidence: 99%

Multivariate max-stable spatial processes

Genton¹,

Padoan²,

Sang³

2015

Biometrika

View full text Add to dashboard Cite

show abstract

“…Max-stable processes assume asymptotic dependence (i.e., dependence will not disappear at very large distances between stations) and usually use variables converted to Fréchet margins. For a detailed overview of max-stable processes, the reader is referred to Padoan (2013). Standard procedures for fitting maxstable processes were established for coordinates and Euclidean distances.…”

Section: Max-stable Modelsmentioning

confidence: 99%

Modeling the spatial dependence of floods using the Fisher copula

Brunner

Furrer

Favre

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

show abstract

“…dependence will not disappear at very large distances) and usually use variables converted to Fréchet margins. For a detailed overview on max-stable processes, the reader is referred to Padoan (2013). Standard procedures for fitting max-stable processes were established for coordinates and Euclidean distances.…”

mentioning

confidence: 99%

Modeling the spatial dependence of floods using the Fisher copula

Brunner¹,

Furrer²,

Favre³

2018

Preprint

View full text Add to dashboard Cite

Abstract. Floods do often not only affect a single location but a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek at proposing a simple model that on the one hand 5 considers this dependence with respect to the network structure of the region and on the other hand, allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of 10 the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula is therefore a suitable model for the stochastic simulation of flood 15 event sets at multiple gauged and ungauged locations.

show abstract

Max‐Stable Processes

Cited by 3 publications

References 27 publications

Multivariate max-stable spatial processes

Multivariate max-stable spatial processes

Modeling the spatial dependence of floods using the Fisher copula

Modeling the spatial dependence of floods using the Fisher copula

Contact Info

Product

Resources

About