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Multiple-hypothesis...
Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation
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- Babu, Prabhu (author)
- Indian Inst Technol, Ctr Appl Res Elect, Delhi 110016, India.
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- Stoica, Peter, 1949- (author)
- Uppsala universitet,Avdelningen för systemteknik
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Indian Inst Technol, Ctr Appl Res Elect, Delhi 110016, India Avdelningen för systemteknik (creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
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In: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 213
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- We consider the problem of model selection for high-dimensional sparse linear regression models. We pose the model selection problem as a multiple-hypothesis testing problem and employ the methods of false discovery rate (FDR) and familywise error rate (FER) to solve it. We also present the reformulation of the FDR/FER-based approaches as criterion-based model selection rules and establish their relation to the extended Bayesian Information Criterion (EBIC), which is a state-of-the-art high-dimensional model selection rule. We use numerical simulations to show that the proposed FDR/FER method is well suited for high-dimensional model selection and performs better than EBIC.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
Keyword
- Model selection
- Sparse parameter estimation
- Mulitple hypothesis testing
- FDR
- FER
Publication and Content Type
- ref (subject category)
- art (subject category)
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