Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910570043670528 |
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| author | Knaeble, Brian Hughes, R. Mitchell Rudolph, George Abramson, Mark A. Razo, Daniel |
| author_facet | Knaeble, Brian Hughes, R. Mitchell Rudolph, George Abramson, Mark A. Razo, Daniel |
| contents | It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09634 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models Knaeble, Brian Hughes, R. Mitchell Rudolph, George Abramson, Mark A. Razo, Daniel Methodology Machine Learning Optimization and Control 62D20, 62J05, 62P99, 62P25 It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models. |
| title | Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models |
| topic | Methodology Machine Learning Optimization and Control 62D20, 62J05, 62P99, 62P25 |
| url | https://arxiv.org/abs/2408.09634 |