Modelling pairwise dependence of maxima in space

Naveau, Philippe; Guillou, Armelle; Cooley, Daniel; Diebolt, Jean

doi:10.1093/biomet/asp001

Cited by 113 publications

(95 citation statements)

References 20 publications

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“…3, is suitable for asymptotically dependent processes, whose renormalized maxima converge to a nontrivial max-stable process. Empirical estimators of θ(h), based on the assumption of max-stability, have been suggested by Schlather and Tawn (2003) and Naveau et al (2009), the latter based on the madogram, an analogue of the variogram for extremal processes. These estimators appear to yield satisfactory results within the max-stable framework, but cannot distinguish different degrees of asymptotic independence.…”

Section: Measures Of Extremal Dependencementioning

confidence: 99%

Geostatistics of Dependent and Asymptotically Independent Extremes

2013

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Spatial modeling of rare events has obvious applications in the environmental sciences and is crucial when assessing the effects of catastrophic events (such as heatwaves or widespread flooding) on food security and on the sustainability of societal infrastructure. Although classical geostatistics is largely based on Gaussian processes and distributions, these are not appropriate for extremes, for which maxstable and related processes provide more suitable models. This paper provides a brief overview of current work on the statistics of spatial extremes, with an emphasis on the consequences of the assumption of max-stability. Applications to winter minimum temperatures and daily rainfall are described.

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Section: Measures Of Extremal Dependencementioning

confidence: 99%

Geostatistics of Dependent and Asymptotically Independent Extremes

2013

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“…This implies that for each day, the simulated temperatures at two or more locations are coherent with each other. This can be achieved with models of the multivariate dependence of several time series (Schölzel and Friederichs, 2008;Naveau et al, 2009;Bonazzi et al, 2012). But such a model needs to be re-evaluated if one set of observations is added or subtracted.…”

Section: Composites Of Temperatures From Analoguesmentioning

confidence: 99%

“…One of the limitation of many random weather generators is their lack of spatial coherence, unless it is imposed on the marginal distributions of a variable at two locations or more (Naveau et al, 2009). Such a spatial constraint is technically difficult to impose, because there is an infinity of choices for models of spatial covariance (Schölzel and Friederichs, 2008).…”

Section: Introductionmentioning

confidence: 99%

AnaWEGE: a weather generator based on analogues of atmospheric circulation

Yiou

2014

Geosci. Model Dev.

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Abstract. This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

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“…An estimate of the full pairwise extremal dependence function is given by the λ-madogram defined in Naveau et al [10] as…”

Section: Doi: 1014736/kyb-2015-2-0193mentioning

confidence: 99%

“…Next, we introduce the generalized madogram which is an extension of Naveau et al's [10]. We also consider a max-stable random field Z = {Z x } x∈R 2 with unit Fréchet margins, but instead of building the pairwise bivariate distribution of this process as provided by the λ-madogram, we consider two regions of locations x = {x 1 , .…”

Section: Generalized Madogram and Dependence Of Spatial Extreme Eventsmentioning

confidence: 99%

Generalized madogram and pairwise dependence of maxima over two regions of a random field

Fonseca¹,

Pereira²,

Ferreira³

et al. 2015

Kybernetika

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Spatial environmental processes often exhibit dependence in their large values. In order to model such processes their dependence properties must be characterized and quantified. In this paper we introduce a measure that evaluates the dependence among extreme observations located in two separated regions of locations of R 2 . We compute the range of this new dependence measure, which extends the existing λ-madogram concept, and compare it with extremal coefficients, finding generalizations of the known relations in pairwise approach. Estimators for this measure are introduced and asymptotic normality and strong consistency are shown. An application to the annual maxima precipitation in Portuguese regions is presented.

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Modelling pairwise dependence of maxima in space

Cited by 113 publications

References 20 publications

Geostatistics of Dependent and Asymptotically Independent Extremes

Geostatistics of Dependent and Asymptotically Independent Extremes

AnaWEGE: a weather generator based on analogues of atmospheric circulation

Generalized madogram and pairwise dependence of maxima over two regions of a random field

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