Sökning: id:"swepub:oai:DiVA.org:uu-510700" >
Multiple-hypothesis...
Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation
-
- Babu, Prabhu (författare)
- Indian Inst Technol, Ctr Appl Res Elect, Delhi 110016, India.
-
- Stoica, Peter, 1949- (författare)
- Uppsala universitet,Avdelningen för systemteknik
-
Indian Inst Technol, Ctr Appl Res Elect, Delhi 110016, India Avdelningen för systemteknik (creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 213
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- 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)
Nyckelord
- Model selection
- Sparse parameter estimation
- Mulitple hypothesis testing
- FDR
- FER
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas