Continuous Spatial Process Models for Spatial Extreme Values

By Huiyan Sang, Alan E. Gelfand. 2010

161 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/23b20010bdf75c94d136c26e01d3d7379aab4fca

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Citations in the corpus

Statistical Modelling of Spatial Extremes (2012, methodology): “…in the Cape Floristic Region of South Africa using the generalized extreme-value distribution with a spatio-temporal hierarchical structure, and in Sang and Gelfand (2009b) used a Gaussian spatial copula model, transformed to the generalised extreme-value scale, to induce dependence between…”
Spatial modeling of extreme snow depth (2011, background): “One approach to introducing dependence is through a spatial copula, as suggested by Sang and Gelfand (2009a), but although this approach is an improvement, Davison, Padoan and Ribatet (2010) show that it can nevertheless lead to inadequate modeling of extreme rainfall.”
Geostatistics of extremes (2012, background): “An exception to this is Sang & Gelfand (2009b), who use a Gaussian copula model to mitigate the independence assumption, but this does not admit a max-stable model for extremes (see the next paragraph).”
Statistical Modeling of Spatial Extremes (2012, methodology): “In accordance with other authors (Zhang (2004); Sang and Gelfand (2010)), we found that it was not possible to learn from the data simultaneously about the parameters α and λ, for which there is an identifiability problem.”
Threshold modeling of extreme spatial rainfall (2013, methodology): “Renard [2011] describes another approach to annual maximum rainfall analysis, based on Bayesian hierarchical models using a copula approach [Sang and Gelfand, 2010] but (1)Ecole Polytechnique F ed erale de Lausanne, EPFL-FSB-MATHAASTAT, Lausanne, Switzerland.”
A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “Because of their ability to represent any marginal distribution, elliptical copulas have become a popular practical tool for modeling multivariate extremes (Ghosh & Mallick, 2011; Renard, 2011; Renard & Lang, 2007; Sang & Gelfand, 2010).”
Non-Stationary Dependence Structures for Spatial Extremes (2014, unknown): “…to induce spatial non-stationarity is to use latent processes or random effects; see Paciorek and Schervish (2006) for non-stationary kriging of annual precipitation data, and Cooley et al. (2007), Sang and Gelfand (2009) and Sang and Gelfand (2010) for applications to extreme value theory.”
Modelling Time Series Extremes (2012, methodology): “The simplest such models treat the data as independent, conditional on these processes, but more sophisticated models using copulas to allow spatial dependence beyond this have been suggested by Sang & Gelfand (2010) and Fuentes et al. (2012).”
Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain (2015, methodology): “1 Elliptical copula for data dependence Elliptical copulas are a flexible tool for modeling multivariate data [Renard, 2011; Sang and Gelfand, 2010; Ghosh and Mallick, 2011; Renard and Lang, 2007].”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, unknown): “(3)) in which logσ follows such a process, but the shape parameter has two values, and many similar models have appeared in the literature (Coles and Casson 1998; Casson and Coles 1999; Sang and Gelfand 2009, 2010).”
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, methodology): “These models often rely on common margins, such as unit Frechét, which may be obtained by probability integral transformation; see Blanchet and Davison (2011), Huser and Davison (2014) and Sang and Gelfand (2010) for examples.”