2018
DOI: 10.1093/biomet/asy026
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High-dimensional peaks-over-threshold inference

Abstract: Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. r-Pareto processes are mathematically simpler and have the potential advantage of incorporating all relevant extreme events, by generalizing the notion of a univariate exceedance. In this paper we investigate score matching for performing high-dimensional peaks over threshold inference, focusing on extreme value proce… Show more

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Cited by 73 publications
(95 citation statements)
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“…The difficulty resides in the computation of high‐dimensional multivariate Gaussian distributions needed for the exponent function V and its partial derivatives Vτi. Unbiased Monte Carlo estimates of these quantities can be obtained, and Thibaud et al () and de Fondeville and Davison () suggest using crude approximations to reduce the computational time while maintaining accuracy; see also Genton et al (), who instead suggest using hierarchical matrix decompositions. Our method is not limited to these two models and could potentially be applied to any max‐stable model for which the functions V and Vτi are known and computable.…”
Section: Discussionmentioning
confidence: 99%
“…The difficulty resides in the computation of high‐dimensional multivariate Gaussian distributions needed for the exponent function V and its partial derivatives Vτi. Unbiased Monte Carlo estimates of these quantities can be obtained, and Thibaud et al () and de Fondeville and Davison () suggest using crude approximations to reduce the computational time while maintaining accuracy; see also Genton et al (), who instead suggest using hierarchical matrix decompositions. Our method is not limited to these two models and could potentially be applied to any max‐stable model for which the functions V and Vτi are known and computable.…”
Section: Discussionmentioning
confidence: 99%
“…Thus, on average, ( c ∞ · C ) −1 simulations from the proposal distribution are needed to obtain an exact sample from the target distribution. Of course, to minimize the computational burden, for a given proposal density truef˜prop, the constant C should be chosen maximal subject to , that is, C=infwdouble-struckRNf˜prop(w)cfmax(w). Recently, de Fondeville and Davison () followed a similar idea and suggested to base the simulation of a general sup‐normalized spectral process Vfalse(·false)maxfalse/false‖Vmax on the relation double-struckP()Vmaxfalse‖Vmaxnormaldv=false‖trueV˜double-struckEfalse‖trueV˜double-struckP()trueV˜false‖trueV˜normaldv, where trueV˜ is a spectral process normalized with respect to another homogeneous functional r instead of the supremum norm, that is, rfalse(trueV˜false)=1 a.s. If false‖trueV˜ is a.s. bounded from above by some constant, from the relation , we obtain an inequality of the same type as for the densities of Vfalse(·false)maxfalse/false‖Vmax and trueV˜false(·false)false/false‖trueV˜…”
Section: Exact Simulation Via Rejection Samplingmentioning
confidence: 99%
“…For a Brown–Resnick process, it is well known that the sum‐normalized process trueV˜ has the same distribution as expfalse(Wpropfalse)false/false‖expfalse(Wpropfalse)1 where W prop has the density f prop from in Section with equal weights p 1 = … = p N = 1/ N (see also Dieker & Mikosch, ). Thus, in this case, the procedure proposed by de Fondeville and Davison () with r ( f ) = ‖ f ‖ 1 is equivalent to performing rejection sampling for Wmax with truef˜prop=i=1N1Nfi as proposal distribution (Algorithm 2A). From (Equation ), it follows that rejection sampling can also be performed with truef˜prop=i=1Npifi and arbitrary positive weights p 1 ,…, p N summing up to 1, because we have with C=mini=1Npi.…”
Section: Exact Simulation Via Rejection Samplingmentioning
confidence: 99%
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