FAST LARGE-SAMPLE GOODNESS-OF-FIT TESTS FOR COPULAS

By Ivan Kojadinovic, Jun Yan, Mark Holmes. 2011

119 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20f7134261df2d30d57db4cbff44d5ca5aa1fec1

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Citations in the corpus

On the return period and design in a multivariate framework (2011, methodology): “The ability of this copula to model the available bivariate data is checked via multivariate goodness-of-ﬁt tests (Berg, 2009; Genest et al., 2009; Kojadinovic et al., 2011): the resulting large p -values indicate that the Gumbel copula C QV cannot be rejected at all standard levels.”
Selection strategies for regular vine copulae (2013, methodology): “…deﬁned as While [Genest and Remillard, 2008] provide a computationally expensive parametric bootstrap procedure for the calculation of the corresponding p -value, [Kojadinovic and Yan, 2011] and [Kojadinovic et al., 2011] developed a multiplier approach to obtain approximate p -values much faster.”
Goodness‐of‐fit testing based on a weighted bootstrap: A fast large‐sample alternative to the parametric bootstrap (2012, methodology): “This resampling technique, based on the multiplier central limit theorem for empirical processes (see e.g. van der Vaart and Wellner, 2000; Kosorok, 2008), was recently used for assessing the goodness of fit of copula models in Kojadinovic, Yan, and Holmes (2011).”
Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River (2015, methodology): “All other copulas from Table II were considered; the results of the Cramér-von Mises test SIn and Kolmogorov–Smirnov test τn for the pair Q-V are presented in Table IV. p-values were calculated based on the parametric bootstrap or multipliers (Kojadinovic et al., 2011) procedure with 10 000 runs.”
Trivariate Frequency Analyses of Peak Discharge, Hydrograph Volume and Suspended Sediment Concentration Data Using Copulas (2014, unknown): “1−exp −u3θ2ð Þð Þ ( ) Asymmetric Clayton-M4 u1−θ2 þ u2−θ2−1 θ1 θ2 þ u3−θ1−1 − 1θ1 can be calculated with the parametric bootstrap procedure defined by Genest and Remillard (2008) or by a little bit faster procedure based on multiplier central limit theorem (Kojadinovic et al. 2011).”