Lasso regularization for mixture experiments with noise variables
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914837952462848 |
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| author | González-Navarrete, Manuel Manríquez-Méndez, Fabián Pereira-Barahona, Manuel |
| author_facet | González-Navarrete, Manuel Manríquez-Méndez, Fabián Pereira-Barahona, Manuel |
| contents | We apply classical and Bayesian lasso regularizations to a family of models with the presence of mixture and process variables. We analyse the performance of these estimates with respect to ordinary least squares estimators by a simulation study and a real data application. Our results demonstrate the superior performance of Bayesian lasso, particularly via coordinate ascent variational inference, in terms of variable selection accuracy and response optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lasso regularization for mixture experiments with noise variables González-Navarrete, Manuel Manríquez-Méndez, Fabián Pereira-Barahona, Manuel Methodology Applications 62Pxx, 62P30 We apply classical and Bayesian lasso regularizations to a family of models with the presence of mixture and process variables. We analyse the performance of these estimates with respect to ordinary least squares estimators by a simulation study and a real data application. Our results demonstrate the superior performance of Bayesian lasso, particularly via coordinate ascent variational inference, in terms of variable selection accuracy and response optimization. |
| title | Lasso regularization for mixture experiments with noise variables |
| topic | Methodology Applications 62Pxx, 62P30 |
| url | https://arxiv.org/abs/2406.12237 |