Goodness-of-fit tests for copulas: A review and a power study

By Christian Genest, Bruno R'emillard, David Beaudoin. 2006

1,426 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/290e9106b1300d77bbad8d5946d80aac4a880dab

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Citations in the corpus

Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, methodology): “The goodness-of-fit test for the copula is performed by Rosenblatt’s probability integral transform (Rosenblatt, 1952; Genest et al., 2009).”
Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, methodology): “The ability of Gumbel family to model the available bivariate data was checked through robust multivariate goodness-of-fit tests (Genest et al. 2009; Berg 2009); the resulting large P-values (shown in Fig.”
Copula based analysis of rainfall severity and duration: a case study (2013, methodology): “In addition, for the extreme–value family of copulas, we also use Cramér–von Mises statistic, Sn introduced in Genest et al. (2011) to perform goodness of fit.”
Spatio-temporal analysis and derivation of copula-based intensity–area–frequency curves for droughts in western Rajasthan (India) (2013, methodology): “An approximate p value for the corresponding test statistic is obtained via large sample simulations using parametric bootstrap method (Genest et al. 2009), which consist of following steps:”
Multivariate drought characteristics using trivariate Gaussian and Student t copulas (2013, methodology): “…2007; De Michele et al., 2005; Grimaldi and Serinaldi, 2006; Shiau et al., 2006; Zhang and Singh, 2006, 2007a, 2007b, 2007c; Genest and Favre, 2007; Genest et al., 2007a, 2007b; Kao and Govindaraju, 2007a, 2007b, 2008; Poulin et al., 2007; Renard and Lang, 2007; Serinaldi and Grimaldi, 2007; Evin…”
Trivariate Frequency Analyses of Peak Discharge, Hydrograph Volume and Suspended Sediment Concentration Data Using Copulas (2014, methodology): “2009): Sn 1⁄4 X i1⁄41 n Cn Ui ð Þ−Cθ Ui ð Þ f g(2); ð3Þ where vector Ui are the pseudo-observations calculated from analysed sample, Cθ is tested theoretical copula (Table 2) andCn is empirical copula, which is defined as (Genest et al. 2009): Cn u ð Þ 1⁄4 1n X i1⁄41 n 1 Ui≤u ð Þ: ð4Þ”
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “In particular, these models have been widely used in multivariate analysis of hydrological data (Genest et al., 2007, 2009; Grimaldi and Serinaldi, 2006; Salvadori et al., 2007; Serinaldi and Grimaldi, 2007; Yan et al., 2007; Zhang and Singh, 2007; Song and Singh, 2010).”
Probabilistic characterization of drought properties through copulas (2009, neutral): “, 6 (Genest and Rémillard, 2008; Genest et al., 2008).”
Models for construction of multivariate dependence – a comparison study (2009, methodology): “We adopt the procedure in Appendix A in Genest et al. (2007), setting the bootstrap parameters m and N to 5000 and 1000, respectively.”
Maximum likelihood estimation of mixed C-vines with application to exchange rates (2012, methodology): “Genest et al. (2009) introduced and studied a λ-function based on the Kendall’s process.”
Copula-based statistical refinement of precipitation in RCM simulations over complex terrain (2011, methodology): “Therefore, the approximate p-values for the test statistic are obtained using a parametric bootstrap (Genest and Remillard, 2008; Genest et al., 2009) as well as a fast multiplier approach (Kojadinovic and Yan, 2011a,b).”