Quantile Regression Tree
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866911969347371008 |
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| author | Okafor, Jaachinma Isegen, Lateefah Ifeanyi, Ark |
| author_facet | Okafor, Jaachinma Isegen, Lateefah Ifeanyi, Ark |
| contents | This study introduces and evaluates the Quantile Regressor Tree (QRT), a novel methodology merging the robust characteristics of quantile regression with the versatility of decision trees. The quantile regressor tree introduces non-linearity to the quantile regression due to the splitting by features in the decision tree, enhancing flexibility while maintaining interpretability. The quantile regression tree gives a parametric and non-parametric mixture of estimating conditional quantiles for high-dimensional predictor variables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14955 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantile Regression Tree Okafor, Jaachinma Isegen, Lateefah Ifeanyi, Ark Applications This study introduces and evaluates the Quantile Regressor Tree (QRT), a novel methodology merging the robust characteristics of quantile regression with the versatility of decision trees. The quantile regressor tree introduces non-linearity to the quantile regression due to the splitting by features in the decision tree, enhancing flexibility while maintaining interpretability. The quantile regression tree gives a parametric and non-parametric mixture of estimating conditional quantiles for high-dimensional predictor variables. |
| title | Quantile Regression Tree |
| topic | Applications |
| url | https://arxiv.org/abs/2402.14955 |