Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918068093976576 |
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| author | Weinberger, Simón Cugliari, Jairo Cain, Aurélie Le |
| author_facet | Weinberger, Simón Cugliari, Jairo Cain, Aurélie Le |
| contents | We present a prediction framework for ordinal models: we introduce optimal predictions using loss functions and give the explicit form of the Least-Absolute-Deviation prediction for these models. Then, we reformulate an ordinal model with functional covariates to a classic ordinal model with multiple scalar covariates. We illustrate all the proposed methods and try to apply these to a dataset collected by EssilorLuxottica for the development of a control algorithm for the shade of connected glasses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles Weinberger, Simón Cugliari, Jairo Cain, Aurélie Le Machine Learning We present a prediction framework for ordinal models: we introduce optimal predictions using loss functions and give the explicit form of the Least-Absolute-Deviation prediction for these models. Then, we reformulate an ordinal model with functional covariates to a classic ordinal model with multiple scalar covariates. We illustrate all the proposed methods and try to apply these to a dataset collected by EssilorLuxottica for the development of a control algorithm for the shade of connected glasses. |
| title | Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.18615 |