The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866910332801253376 |
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| author | Obenchain, Robert L. |
| author_facet | Obenchain, Robert L. |
| contents | A new generalized ridge regression shrinkage path is proposed that is as short as possible under the restriction that it must pass through the vector of regression coefficient estimators that make the overall Optimal Variance-Bias Trade-Off under Normal distribution-theory. Five distinct types of ridge TRACE displays plus other graphics for this efficient path are motivated and illustrated here. These visualizations provide invaluable data-analytic insights and improved self-confidence to researchers and data scientists fitting linear models to ill-conditioned (confounded) data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_05161 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk Obenchain, Robert L. Methodology Computation Machine Learning 62J07, 62J20, 62-04 A new generalized ridge regression shrinkage path is proposed that is as short as possible under the restriction that it must pass through the vector of regression coefficient estimators that make the overall Optimal Variance-Bias Trade-Off under Normal distribution-theory. Five distinct types of ridge TRACE displays plus other graphics for this efficient path are motivated and illustrated here. These visualizations provide invaluable data-analytic insights and improved self-confidence to researchers and data scientists fitting linear models to ill-conditioned (confounded) data. |
| title | The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk |
| topic | Methodology Computation Machine Learning 62J07, 62J20, 62-04 |
| url | https://arxiv.org/abs/2103.05161 |