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### 分位数回归提升树模型及应用

1. 山东工商学院统计学院, 烟台 264005
• 收稿日期:2021-09-10 修回日期:2021-12-27 出版日期:2022-05-25 发布日期:2022-07-23
• 通讯作者: 蔡超,Email:caichao622@126.com.
• 基金资助:
国家社会科学基金项目(20BTJ052),山东省社会科学规划研究项目(20CTJJ01),全国统计科学研究一般项目(2019LY101)资助课题.

CAI Chao, HUANG Congcong, DONG Haotian. Quantile Regression Boosting Tree and Its Application[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(5): 1216-1233.

### Quantile Regression Boosting Tree and Its Application

CAI Chao, HUANG Congcong, DONG Haotian

1. School of Statistics, Shandong Technology and Business University, Yantai 264005
• Received:2021-09-10 Revised:2021-12-27 Online:2022-05-25 Published:2022-07-23

In order to solve the shortcomings of the low prediction performance of the quantile regression tree model and the high computational cost of the quantile regression gradient boosting tree model, this paper proposes the quantile regression boosting tree model (QRBT) based on the quantile regression and boosting tree model and its specific algorithm is given. On the one hand, the optimization process of this model is simpler, which saves the calculation cost. On the other hand, the sum of multiple quantile regression tree models improves the prediction performance. Through numerical simulation and application research, it is found that the QRBT model can obtain higher estimation and prediction accuracy compared with quantile regression, quantile regression tree, and quantile regression gradient boosting tree model. In addition, compared with quantile regression gradient boosting tree model, QRBT model significantly reduces the running time.

MR(2010)主题分类:

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