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

Antoine Bichat, Christophe Ambroise and Mahendra Mariadassou ()
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Antoine Bichat: LaMME, Université d’Évry val d’Essonne
Christophe Ambroise: LaMME, Université d’Évry val d’Essonne
Mahendra Mariadassou: MaIAGE, INRAE, Université Paris-Saclay

Computational Statistics, 2022, vol. 37, issue 3, No 1, 995-1013

Abstract: 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 .

Keywords: Multiple testing; Ornstein-Uhlenbeck process; Lasso; Debiasing; FDR control; Metagenomic (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01148-6

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DOI: 10.1007/s00180-021-01148-6

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