Simultaneous false discovery proportion bounds via knockoffs and closed testing

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Li, Jinzhou, Maathuis, Marloes H., Goeman, Jelle J.
Format: Preprint
Published: 2022
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866913242224263168
author Li, Jinzhou
Maathuis, Marloes H.
Goeman, Jelle J.
author_facet Li, Jinzhou
Maathuis, Marloes H.
Goeman, Jelle J.
contents We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's $k$-familywise error rate control method and interpolation. We then generalize it by considering a collection of $k$ values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local test statistic. This allows us to obtain a further uniform improvement and other generalizations over previous methods. We also develop an efficient shortcut for its implementation. We compare the performance of our proposed methods in simulations and apply them to a data set from the UK Biobank.
format Preprint
id arxiv_https___arxiv_org_abs_2212_12822
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Simultaneous false discovery proportion bounds via knockoffs and closed testing
Li, Jinzhou
Maathuis, Marloes H.
Goeman, Jelle J.
Methodology
We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's $k$-familywise error rate control method and interpolation. We then generalize it by considering a collection of $k$ values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local test statistic. This allows us to obtain a further uniform improvement and other generalizations over previous methods. We also develop an efficient shortcut for its implementation. We compare the performance of our proposed methods in simulations and apply them to a data set from the UK Biobank.
title Simultaneous false discovery proportion bounds via knockoffs and closed testing
topic Methodology
url https://arxiv.org/abs/2212.12822