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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00256 |
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| _version_ | 1866913569561378816 |
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| author | Liu, Zihe Saha, Diptarka Liang, Feng |
| author_facet | Liu, Zihe Saha, Diptarka Liang, Feng |
| contents | This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature's contribution to the response is zero, linear, or nonlinear. Further, an efficient coordinate descent algorithm is introduced to implement VARD. Empirical evaluations on simulated and real-world data underscore VARD's superiority over alternative variable selection methods for additive models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination Liu, Zihe Saha, Diptarka Liang, Feng Methodology This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature's contribution to the response is zero, linear, or nonlinear. Further, an efficient coordinate descent algorithm is introduced to implement VARD. Empirical evaluations on simulated and real-world data underscore VARD's superiority over alternative variable selection methods for additive models. |
| title | Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination |
| topic | Methodology |
| url | https://arxiv.org/abs/2411.00256 |