A robust test for asymptotic independence of bivariate extremes

Tsai, Yu-Ling; Dupuis, Debbie J.; Murdoch, Duncan J.

doi:10.1080/02331888.2011.568118

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…For more information for testing about covariance matrices in p−dimensional data one can see for example, Ledoit et al (2002), Bai et al [11], Chen et al [12], Jiang et al [13] and Jiang and Yang [14]. Tsai et al [15], showed that the existing tests for asymptotic independence are sensitive to outliers. A robust test proposed.…”

Section: Introductionmentioning

confidence: 99%

An Asymptotic Two-Sided Test in a Family of Multivariate Distribution

Bazyari¹,

Afshari²,

Samuh³

2020

JSTA

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In the present paper, a two-sided test in a family of multivariate distribution according to the Mahalanobis distance with mean vector and positive definite matrix is considered. First, a family of multivariate distribution is introduced, then using the likelihood ratio method a test statistic is computed. The distribution of the test statistic is proposed for different sample sizes and fixed dimension. We study the distribution approximation computed using the likelihood ratio test and an efficient algorithm to compute the density functions can be derived according to Witkovsk´y, J. Stat. Plan. Inference. 94 ( 2001), 1-13. Also, a simulation study is presented on the sample sizes and powers to compare the performance of tests and show that the proposed distribution approximation is better than the classical distribution approximation.

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Section: Introductionmentioning

confidence: 99%

An Asymptotic Two-Sided Test in a Family of Multivariate Distribution

Bazyari¹,

Afshari²,

Samuh³

2020

JSTA

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Multivariate Extreme Value Theory - A Tutorial with Applications to Hydrology and Meteorology

Dutfoy

Parey²,

Roche³

2014

Dependence Modeling

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In this paper, we provide a tutorial on multivariate extreme value methods which allows to estimate the risk associated with rare events occurring jointly. We draw particular attention to issues related to extremal dependence and we insist on the asymptotic independence feature. We apply the multivariate extreme value theory on two data sets related to hydrology and meteorology: first, the joint flooding of two rivers, which puts at risk the facilities lying downstream the confluence; then the joint occurrence of high speed wind and low air temperatures, which might affect overhead lines.

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A robust test for asymptotic independence of bivariate extremes

Cited by 2 publications

References 12 publications

An Asymptotic Two-Sided Test in a Family of Multivariate Distribution

An Asymptotic Two-Sided Test in a Family of Multivariate Distribution

Multivariate Extreme Value Theory - A Tutorial with Applications to Hydrology and Meteorology

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