Mixture of partially linear experts
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909580776177664 |
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| author | Hwang, Yeongsan Seo, Byungtae Oh, Sangkon |
| author_facet | Hwang, Yeongsan Seo, Byungtae Oh, Sangkon |
| contents | In the mixture of experts model, a common assumption is the linearity between a response variable and covariates. While this assumption has theoretical and computational benefits, it may lead to suboptimal estimates by overlooking potential nonlinear relationships among the variables. To address this limitation, we propose a partially linear structure that incorporates unspecified functions to capture nonlinear relationships. We establish the identifiability of the proposed model under mild conditions and introduce a practical estimation algorithm. We present the performance of our approach through numerical studies, including simulations and real data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02905 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixture of partially linear experts Hwang, Yeongsan Seo, Byungtae Oh, Sangkon Methodology Machine Learning In the mixture of experts model, a common assumption is the linearity between a response variable and covariates. While this assumption has theoretical and computational benefits, it may lead to suboptimal estimates by overlooking potential nonlinear relationships among the variables. To address this limitation, we propose a partially linear structure that incorporates unspecified functions to capture nonlinear relationships. We establish the identifiability of the proposed model under mild conditions and introduce a practical estimation algorithm. We present the performance of our approach through numerical studies, including simulations and real data analysis. |
| title | Mixture of partially linear experts |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2405.02905 |