On the likelihood function of Gaussian max-stable processes

By Marc G. Genton, Yanyuan Ma, Huiyan Sang. 2011

122 citations recorded by the source corpus.

Subfields: Applied Mathematics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a9772edd7f60eba9f9f106e5a5c10420ef7a2951

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Citations in the corpus

Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes (2009, methodology): “As formulated, Z ( x ) has unit Fr´echet margins and its bivariate and trivariate marginal distributions can be used to construct a composite likelihood [Padoan et al., 2010; Genton et al., 2011].”
Efficient inference and simulation for elliptical Pareto processes (2013, methodology): “Hence, inference is usually made using a less efficient composite likelihood approach (Padoan et al., 2010; Genton et al., 2011; Huser & Davison, 2013a).”
Estimation of Hüsler–Reiss distributions and Brown–Resnick processes (2012, methodology): “Hence, for ML estimation based on block maxima, only the bivariate or sometimes the trivariate (see Genton et al. (2011)) densities are used in the framework of a CL approach.”
Geostatistics of extremes (2012, background): “…for computation of the necessary derivatives was available, the complexity of analytical computation of equation (2.5) rapidly mounts with D: it is feasible for a variety of simple random processes W (x) when D = 2, but is possible only for special processes for larger D (Genton et al. 2011).”
Modelling spatial extreme events with environmental applications (2018, methodology): “A weakness with this model is that G can only be specified via a series of evaluations of the multivariate normal distribution function (Genton et al., 2011), though reductions in the numerical difficulties can be achieved using methods of Wadsworth and Tawn (2012) that require additional informatio”
The normal law under linear restrictions: simulation and estimation via minimax tilting (2016, background): “…Lindgren, 2015); inference for spacial processes (Wadsworth and Tawn, 2014); likelihood estimation for max-stable processes (Huser and Davison, 2013; Genton et al., 2011); computation of simultaneous conﬁdence bands (Aza¨ıs et al., 2010); uncertainty regions for latent Gaussian models (Bolin and…”
Non-Stationary Dependence Structures for Spatial Extremes (2014, background): “(4) The exponent measure has a closed-form formula for specific choices of W (s); see, e.g., Schlather (2002), Genton et al. (2011), Huser and Davison (2013), and Opitz (2013).”
Space–time modelling of extreme events (2012, methodology): “The full likelihood appears intractable for most max-stable processes (but see Genton et al. (2011) and Huser and Davison (2013)).”
Spatial extremes: Max-stable processes at work (2013, methodology): “Likelihood based inferences are widely used either in a frequentist context Genton et al. (2011); Davison et al. (2012); Padoan et al. (2010) or in a Bayesian framework Ribatet et al. (2012); Erhardt and Smith (2012).”
Composite likelihood estimation for the Brown–Resnick process (2013, methodology): “If p > d+ 1, the simplex is always degenerate (Genton et al., 2011).”
Conditional simulation of max-stable processes (2012, methodology): “Apart from the Smith model (Genton et al., 2011), only the bivariate cumulative distribution functions are explicitly known.”