Hierarchical modeling for extreme values observed over space and time

By Huiyan Sang, Alan E. Gelfand. Environmental and Ecological Statistics, 2009

229 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c3a7d2578bde8ca2cd600638cd529cbcf4b422b1

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Citations in the corpus

Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes (2009, background): “…assumption in the data layer, yielding a product of K independent GEV densities, analogous to Cooley, Nychka, and Naveau (2007) or Sang and Gelfand (2009); M2 the max-stable process hierarchical model with no adjustment; M3 the max-stable process hierarchical model with an adaptive…”
Max-stable processes for modelling extremes observed in space and time (2011, methodology): “Sang and Gelfand [37] propose a hierarchical modelling procedure, where on a latent stage spatio-temporal dependence is included via the parameters of the generalized extreme value distribution.”
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.”
Continuous Spatial Process Models for Spatial Extreme Values (2010, methodology): “Sang and Gelfand (2008) offers detailed discussion with regard to these latent specifications.”
Statistical Modeling of Spatial Extremes (2012, methodology): “Alternatively, Bayesian inference can be performed; for example, Sang and Gelfand (2010) use Markov chain Monte Carlo to fit such a model, with the Gaussian copula, exponential correlation function and GEV marginal distributions having the same scale and shape parameters but a regression structure a”
A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “…on the generalized extreme value (GEV) distribution due to its ability to capture a wide range of tail behaviors and its use in previous hydrologic studies (Davison et al., 2012; Ghosh & Mallick, 2011; Katz, 2010; Katz et al., 2002; Najafi & Moradkhani, 2014; Renard, 2011; Sang & Gelfand, 2009).”
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…”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, background): “(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).”
Modelling Time Series Extremes (2012, background): “Examples are Coles & Casson (1998), Casson & Coles (1999), Fawcett & Walshaw (2006a), Cooley et al. (2006), Cooley et al. (2007) and Sang & Gelfand (2009).”