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The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control

Machkour, Jasin ; Muma, Michael ; Palomar, Daniel P. (2022)
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control.
doi: 10.48550/arXiv.2110.06048
Report, Bibliographie

Kurzbeschreibung (Abstract)

We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for a high power. We prove under mild conditions that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.

Typ des Eintrags: Report
Erschienen: 2022
Autor(en): Machkour, Jasin ; Muma, Michael ; Palomar, Daniel P.
Art des Eintrags: Bibliographie
Titel: The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
Sprache: Englisch
Publikationsjahr: 17 Oktober 2022
Verlag: arXiv
Reihe: Methodology
Auflage: 5. Version
DOI: 10.48550/arXiv.2110.06048
URL / URN: https://arxiv.org/abs/2110.06048v5
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Kurzbeschreibung (Abstract):

We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for a high power. We prove under mild conditions that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.

Freie Schlagworte: emergenCITY_CPS, emergenCITY
Zusätzliche Informationen:

Preprint, Titel mit Version 5 geändert; Forschungsdaten ab Version 7; Version 1-4 : The Terminating-Knockoff Filter: Fast High-Dimensional Variable Selection with False Discovery Rate Control ; ebenfalls in TUbiblio verzeichnet

Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science
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LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Zentrale Einrichtungen
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ)
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner
Hinterlegungsdatum: 08 Mär 2023 07:42
Letzte Änderung: 13 Mai 2024 12:27
PPN: 50925957X
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