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Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software

Author

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  • Claude Renaux

    (ETH Zürich)

  • Laura Buzdugan

    (ETH Zürich)

  • Markus Kalisch

    (ETH Zürich)

  • Peter Bühlmann

    (ETH Zürich)

Abstract

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  • Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 59-67, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00948-1
    DOI: 10.1007/s00180-019-00948-1
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    References listed on IDEAS

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    1. Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
    2. Rajen D. Shah & Richard J. Samworth, 2013. "Variable selection with error control: another look at stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 55-80, January.
    3. Ryan J. Tibshirani & Jonathan Taylor & Richard Lockhart & Robert Tibshirani, 2016. "Exact Post-Selection Inference for Sequential Regression Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 600-620, April.
    4. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    5. Jonas R. Klasen & Elke Barbez & Lukas Meier & Nicolai Meinshausen & Peter Bühlmann & Maarten Koornneef & Wolfgang Busch & Korbinian Schneeberger, 2016. "A multi-marker association method for genome-wide association studies without the need for population structure correction," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
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    Cited by:

    1. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.

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