The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control Article Swipe
Jasin Machkour
,
Michael Muma
,
Daniel P. Palomar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.06048
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.06048
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 high power. We prove 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 in numerical experiments and 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.
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- Type
- preprint
- Language
- en
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286905895
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https://openalex.org/W4286905895Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2110.06048Digital Object Identifier
- Title
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The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate ControlWork title
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preprintOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-10-12Full publication date if available
- Authors
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Jasin Machkour, Michael Muma, Daniel P. PalomarList of authors in order
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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-
https://arxiv.org/pdf/2110.06048Direct OA link when available
- Concepts
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Normalization property, False discovery rate, Selection (genetic algorithm), Computer science, Variable (mathematics), Algorithm, Control (management), Feature selection, Mathematics, Artificial intelligence, Chemistry, Biochemistry, Mathematical analysis, Programming language, GeneTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2024: 2, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Experiments | 4 |
| abstract_inverted_index.association | 146 |
| abstract_inverted_index.combination | 52 |
| abstract_inverted_index.computation | 152 |
| abstract_inverted_index.expectation | 112 |
| abstract_inverted_index.experiments | 47, 140 |
| abstract_inverted_index.genome-wide | 145 |
| abstract_inverted_index.outperforms | 132 |
| abstract_inverted_index.predictors. | 64 |
| abstract_inverted_index.probability | 108 |
| abstract_inverted_index.simulations | 81 |
| abstract_inverted_index.TRexSelector | 174 |
| abstract_inverted_index.distribution | 109 |
| abstract_inverted_index.experiments. | 45 |
| abstract_inverted_index.user-defined | 20 |
| abstract_inverted_index.computational | 116 |
| abstract_inverted_index.implementation | 177 |
| abstract_inverted_index.high-dimensional | 13 |
| abstract_inverted_index.state-of-the-art | 133 |
| abstract_inverted_index.Terminating-Random | 3 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |