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Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation
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DOI：10.1016/j.sigpro.2023.109189.png)
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摘要
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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.
Keyword:
Model selection
Sparse parameter estimation
Mulitple hypothesis testing
FDR
FER
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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