A multiplicative log-Laplace nugget added to random scale-mixture processes for spatial extremes produces a censored likelihood that factors into closed-form univariate densities while preserving the original extremal dependence structure.
Marginal and Joint Tail Equivalence Recall that X(s) =ϵ(s)X ∗(s), whereX ∗(s)is the latent smooth process with regularly varying tail andϵ(s)is the log-Laplace nugget
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Log-Laplace Nuggets for Fully Bayesian Fitting of Spatial Extremes Models to Threshold Exceedances
A multiplicative log-Laplace nugget added to random scale-mixture processes for spatial extremes produces a censored likelihood that factors into closed-form univariate densities while preserving the original extremal dependence structure.