Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology

By Benjamin Renard, Matthew Lang. 2007

342 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/12614a6da7db7af5114b48296e120607e55446bf

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In Environmental Engineering

In Global and Planetary Change

In Statistics and Probability

Citations in the corpus

A new rainfall model based on the Neyman‐Scott process using cubic copulas (2008, background): “Recently, copulas appeared in hydrology [e.g., De Michele and Salvadori, 2003; Favre et al., 2004a; Grimaldi and Serinaldi, 2006; Genest and Favre, 2007; Renard and Lang, 2007].”
Multivariate multiparameter extreme value models and return periods: A copula approach (2010, methodology): “[12] It is fundamental to note that not all copulas are MEV (i.e., satisfy the max‐stability property (2) (see, e.g., many of the examples by Nelsen [2006] and the illustrations given by Salvadori et al. [2007])), and consequently cannot be used to constructconsistentMEVmodels.Forinstance,adistribut”
Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain (2015, methodology): “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].”
Copula-based statistical refinement of precipitation in RCM simulations over complex terrain (2011, methodology): “Copula-based models have been introduced for multivariate frequency analysis, risk assessment, geostatistical interpolation and multivariate extreme value analyses (e.g. De Michele and Salvadori , 2003; Bárdossy, 2006; Genest and Favre , 2007; Renard and Lang, 2007; Scḧolzel and Friederichs , 2008; ”
A Bayesian hierarchical approach to regional frequency analysis (2011, methodology): “The first assumption corresponds to using a Gaussian copula [e.g., Renard and Lang, 2007] with dependence matrix R : ðY ½~s1; t ; . . . ;Y ½~sM ; t Þ GCopM ðR; ½~si ; ½~si ; ½~si f gi¼1:M Þ: ð17Þ [95] The second assumption corresponds to using a Student copula with dependence matrix R and tail…”
A copula-based joint deficit index for droughts. (2010, methodology): “The covariance matrix R can be estimated from rank based statistics such as Spearman’s correlation coefficient r instead of Pearson’s linear correlation coefficient q (see Renard and Lang, 2007).”
Extremes on river networks (2015, methodology): “and Resnick (1977), Kabluchko, Schlather and de Haan (2009)].”
Copula‐based drought severity‐duration‐frequency analysis in Iran (2009, background): “…2004; Salvadori and de Michele, 2004a, 2004b; De Michele et al., 2005; Shiau et al., 2006; Grimaldi and Serinaldi, 2006; Salvadori and de Michele, 2006; Zhang and Singh, 2006; Kao and Govindaraju, 2007; Renard and Lang, 2007; Zhang and Singh, 2007; Evin and Favre, 2008; Kao and Govindaraju, 2008).”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, background): “Renard & Lang (2007) presented case studies that utilized the Gaussian copula-based multivariate hydrological distributions for field significance determination, regional risk estimation, discharge–duration–frequency (QdF) curves, and for regional frequency analysis.”
Multivariate non-normally distributed random variables in climate research - introduction to the copula approach (2008, background): “This is the reason why the copula approach is very popular in modeling multivariate extremes (e.g. Renard and Lang, 2007).”
Is southwestern China experiencing more frequent precipitation extremes? (2014, methodology): “In recent years, copulas have been used for analyzing hydro-meteorological extremes widely (Madadgar and Moradkhani 2013, Renard and Lang 2007, Scholzel and Friederichs 2008, Zhang and Singh 2007, Zhang et al 2012b, 2013b).”