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Author: Dickhaus, Thorsten × End date: 2019 × Start date: 2011 × DDC: 510 × Subject: multiple testing ×

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    Simultaneous statistical inference for epigenetic data
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2015) Schildknecht, Konstantin; Olek, Sven; Dickhaus, Thorsten
    Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
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    Uncertainty quantification for the family-wise error rate in multivariate copula models
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Stange, Jens; Bodnar, Taras; Dickhaus, Thorsten
    We derive confidence regions for the realized family-wise error rate (FWER) of certain multiple tests which are empirically calibrated at a given (global) level of significance. To this end, we regard the FWER as a derived parameter of a multivariate parametric copula model. It turns out that the resulting onfidence regions are typically very much concentrated around the target FWER level, while generic multiple tests with fixed thresholds are in general not FWER-exhausting. Since FWER level exhaustion and optimization of power are equivalent for the classes of multiple test problems studied in this paper, the aforementioned findings militate strongly in favour of estimating the dependency structure (i. e., copula) and incorporating it in a multivariate multiple test procedure. We illustrate our theoretical results by considering two particular classes of multiple test problems of practical relevance in detail, namely, multiple tests for components of a mean vector and multiple support tests.
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    On the Simes inequality in elliptical models
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Bodnar, Taras; Dickhaus, Thorsten
    We provide necessary and sufficient conditions for the validity of the inequality of Simes (1986) in models with elliptical dependencies. Necessary conditions are presented in terms of sufficient conditions for the reverse Simes inequality. One application of our main results concerns the problem of model misspecification, in particular the case that the assumption of Gaussianity of test statistics is violated. Since our sufficient conditions require nonnegativity of correlation coefficients between test statistics, we also develop exact tests for vectors of correlation coefficients.