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Relative Cost Based Model Selection For Sparse High-Dimensional Linear Regression Models

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IEEE ICASSP 2020 Virtual Conference May 2020

In this paper, we propose a novel model selection method named multi-beta-test (MBT) for the sparse high-dimensional linear regression model. The estimation of the correct subset in the linear regression problem is formulated as a series of hypothesis tes

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Relative Cost Based Model Selection For Sparse High-Dimensional Linear Regression Models

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In this paper, we propose a novel model selection method named multi-beta-test (MBT) for the sparse high-dimensional linear regression model. The estimation of the correct subset in the linear regression problem is formulated as a series of hypothesis tes