Robust Sparse Regression with High-Breakdown Value |
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Authors: | Weiyan Mu |
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Affiliation: | School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China |
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Abstract: | Penalized least squares estimators are sensitive to the influence of outliers like the ordinary least squares estimator. We propose a sparse regression estimator for robust variable selection and estimation based on a robust initial estimator. It is proven that our estimator has at least the same breakdown value as the initial estimator. Numerical examples are presented to illustrate our method. |
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Keywords: | Adaptive estimation Breakdown value Non negative garrote Variable selection |
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