Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

By Christian Genest, Anne-Catherine Favre. 2007

1,570 citations recorded by the source corpus.

Subfields: Environmental Engineering, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9243f13511e10e46be4df14a0a5dd94cdbd03d15

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Citations in the corpus

Multivariate multiparameter extreme value models and return periods: A copula approach (2010, methodology): “[46] In Figure 2 we show the nonparametric bivariate rank plots [Genest and Favre, 2007] of all the six pairs of interest here.”
Copula based analysis of rainfall severity and duration: a case study (2013, methodology): “The use of univariate distributions has become the standard approach in rainfall analysis; therefore, despite it being more relevant to climactic related problems, the application of multivariate distributions has not been pursued to any great extent (c.f. Zhang and Singh 2007; Genest and Favre 2007”
Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, methodology): “An independence test based on the empirical Kendall’s τ in [34] is performed before the copula selection.”
A copula-based joint deficit index for droughts. (2010, background): “, 2006; Zhang and Singh, 2006; Renard and Lang, 2007), trivariate frequency analysis (Grimaldi and Serinaldi, 2006a; Salvadori and De Michele, 2006; Genest et al., 2007; Kao and Govindaraju, 2008), bivariate return periods (Salvadori and De Michele, 2004), groundwater parameters (Bárdossy, 2006), dr”
Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas (2012, methodology): “Genest and Favre (2007) presented a comprehensive review of copula with their parameter estimation and inference procedures, with an application to a case study in Harricana watershed, Québec, Canada.”
Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, background): “Other, more compact reviews include Genest and Favre (2007), H¨ardle and Okhrin (2009) and Embrechts (2009).”
Multivariate spearman's ρ for aggregating ranks using copulas (2016, background): “It can be shown (Nelsen, 2006; Genest and Favre, 2007) that ρn is an asymptotically unbiased estimator of ρ = 12 ∫ [0,1]2 C(u, v) du dv − 3, where C is the population version of Cn.”
The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data (2011, background): “…family allows for a wide variety of dependence structures ranging from lower tail dependence (Clayton), through no tail dependence (Frank), to upper tail dependence (Gumbel), and seems to be well applicable to many problems in geosciences (Genest and Favre 2007, Schölzel and Friederichs 2008).”
A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, background): “Pioneering work in copula‐based bivariate frequency analysis are the studies of De Michele and Salvadori [2003] and Favre et al. [2004], followed by several other contributions [e.g., De Michele et al., 2005; Genest and Favre, 2007; Kao and Govindaraju, 2007; Zhang and Singh, 2007a].”
Inference in multivariate Archimedean copula models (2011, background): “This is known to lead to robust, margin-free estimation and testing procedures ( Genest and Favre 2007; Kim et al. 2007).”
Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy) (2017, neutral): “…, 1] is a joint CDF of an n-dimensional random vector on the unit cube [0 , 1 with uniform marginals (Joe, 2014; Salvadori et al., 2007; Nelsen, 2006; Genest et al., 2007; Salvadori and De Michele, 2007 Under the assumption that the marginal distributions F i are continuous, the copula C is…”