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On Familywise Error Rate Cutoffs under Pairwise Exchangeability

Author

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  • Thomas Fung

    (Macquarie University)

  • Eugene Seneta

    (The University of Sydney)

Abstract

In a pairwise exchangeable dependence setting for test statistics, the cutoffs of Sarkar et al. (2016) may be viewed as a first iteration improvement of Holm (1979)’s classical cutoffs under a convexity condition on the copula. The cutoffs of Seneta and Chen (1997) which improve Holm’s in the present exchangeability setting, are shown, after an analogous first iteration step, to lead to a refinement of Sarkar et al. (2016). Further, we show that the convexity condition can be circumvented in practice, computationally. Improvement by iteration limit of cutoffs is considered for both procedures. Comparisons between the effects of the several cutoff sets are made by way of plots of the familywise error rate against correlation $$\rho$$ ρ in the classic setting of the multivariate Normal; and the distributional setting of the multivariate Generalized Hyperbolic for the important Variance Gamma type subfamily, for which a convexity condition cannot be analytically verified.

Suggested Citation

  • Thomas Fung & Eugene Seneta, 2023. "On Familywise Error Rate Cutoffs under Pairwise Exchangeability," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-13, June.
    • Handle: RePEc:spr:metcap:v:25:y:2023:i:2:d:10.1007_s11009-023-10018-1
    DOI: 10.1007/s11009-023-10018-1
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    References listed on IDEAS

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    1. Fung, Thomas & Seneta, Eugene, 2021. "Tail asymptotics for the bivariate equi-skew generalized hyperbolic distribution and its Variance-Gamma special case," Statistics & Probability Letters, Elsevier, vol. 178(C).
    2. Thomas Fung & Eugene Seneta, 2010. "Modelling and Estimation for Bivariate Financial Returns," International Statistical Review, International Statistical Institute, vol. 78(1), pages 117-133, April.
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