Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation

doi:10.1016/j.sigpro.2023.109189

Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation

2023-12-01

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DOI：10.1016/j.sigpro.2023.109189

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Abstract

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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.

Keywords:

Model selection

Sparse parameter estimation

Mulitple hypothesis testing

FDR

FER

Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation

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