A moment-based test for extreme-value dependence

Du, Yeting; Nešlehová, Johanna

doi:10.1007/s00184-012-0410-z

Cited by 3 publications

(6 citation statements)

References 15 publications

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“…below, taken fromCormier et al (2014), gathers those results fromCormier et al (2014),Kojadinovic and Yan (2010b),Du and Nešlehová (2013),and Bücher et al (2011) that were obtained under the same experimental settings (notice…”

mentioning

confidence: 73%

“…The above approach was recently furthered by Du and Nešlehová (2013) who used the first three moments of Kendall's distribution and the theoretical relationship…”

Section: Approaches Based On Kendall's Distributionmentioning

confidence: 99%

“…The latter implies that tests in this class are not consistent. Despite that fact, the Monte Carlo experiments reported in Kojadinovic and Yan (2010b) and in Du and Nešlehová (2013) suggest that tests based on S 2n and its extension studied in Du and Nešlehová (2013) are among the most powerful procedures for testing bivariate extreme-value dependence.…”

Section: Approaches Based On Kendall's Distributionmentioning

confidence: 99%

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Estimation of Extreme Conditional Quantiles

Wang¹,

Li²

2016

Extreme Value Modeling and Risk Analysis

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An overview of existing nonparametric tests of extreme-value dependence is presented. Given an i.i.d. sample of random vectors from a continuous distribution, such tests aim at assessing whether the underlying unknown copula is of the extreme-value type or not. The existing approaches available in the literature are summarized according to how departure from extreme-value dependence is assessed. Related statistical procedures useful when modeling data with this type of dependence are briefly described next. Two illustrations on real data sets are then carried out using some of the statistical procedures under consideration implemented in the R package copula. Finally, the related problem of testing the maximum domain of attraction condition is discussed.

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mentioning

confidence: 73%

“…The above approach was recently furthered by Du and Nešlehová (2013) who used the first three moments of Kendall's distribution and the theoretical relationship…”

Section: Approaches Based On Kendall's Distributionmentioning

confidence: 99%

Section: Approaches Based On Kendall's Distributionmentioning

confidence: 99%

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Estimation of Extreme Conditional Quantiles

Wang¹,

Li²

2016

Extreme Value Modeling and Risk Analysis

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“…This allows for direct comparisons with a host of alternative tests of extremeness proposed in Ben Ghorbal et al (2009), Kojadinovic and Yan (2010), Bücher et al (2011), Kojadinovic et al (2011), Quessy (2012, Du and Nešlehová (2013), and described in the Appendix.…”

Section: Test Of Extremenessmentioning

confidence: 99%

“…. , (X n , Y n ) is a sample from a random pair (X, Y ) with distribution function H , continuous margins and a copula C. BGN and DN tests (Ben Ghorbal et al 2009;Du and Nešlehová 2013) These tests are based on the moments of W = H (X, Y ), which are simple functions of Kendall's tau when C is an extreme-value copula . Let H n denote the empirical distribution function of the sample (X 1 , Y 1 ), .…”

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confidence: 99%

Using B-splines for nonparametric inference on bivariate extreme-value copulas

2014

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A visual tool is proposed for detecting the presence of extreme-value dependence or extremal tail behavior in bivariate data. The points appearing on the plot stem from rank-based transformations of the observations and can serve to estimate the unknown Pickands dependence function of the underlying extreme-value copula or its attractor. Quadratic constrained B-spline smoothing is used to derive an intrinsic estimator, which naturally leads to a test of extremeness. Both the estimator and the test are seen to perform well in simulations. The proposed methodology is illustrated with real data and the treatment of ties is briefly discussed.

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