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Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process

Author

Listed:
  • Antoine Bichat

    (LaMME, Université d’Évry val d’Essonne
    Enterome)

  • Christophe Ambroise

    (LaMME, Université d’Évry val d’Essonne)

  • Mahendra Mariadassou

    (MaIAGE, INRAE, Université Paris-Saclay)

Abstract

Statistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou .

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01148-6
    DOI: 10.1007/s00180-021-01148-6
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    References listed on IDEAS

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