.png){kind=icon}
返回
Quantile estimators with orthogonal pinball loss function
.png){kind=icon}
PRE
AI
DOI：10.1002/for.2510.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To guarantee stable quantile estimations even for noisy data, a novel loss function and novel quantile estimators are developed, by introducing the effective concept of orthogonal loss considering the noise in both response and explanatory variables. In particular, the pinball loss used in classical quantile estimators is improved into novel orthogonal pinball loss (OPL) by replacing vertical loss by orthogonal loss. Accordingly, linear quantile regression (QR) and support vector machine quantile regression (SVMQR) can be respectively extended into novel OPL-based QR and OPL-based SVMQR models. The empirical study on 10 publicly available datasets statistically verifies the superiority of the two OPL-based models over their respective original forms in terms of prediction accuracy and quantile property, especially for extreme quantiles. Furthermore, the novel OPL-based SVMQR model with both OPL and artificial intelligence (AI) outperforms all benchmark models, which can be used as a promising quantile estimator, especially for noisy data.
Keyword:
orthogonal pinball loss function
probability forecasting
quantile estimation
robustness
support vector machine quantile regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
2.3K
被引数:
3.0K
机构
引用论文
A normal least squares support vector machine (NLS-SVM) and its learning algorithm.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏On downside risk predictability through liquidity and trading activity: A dynamic quantile approach
PREAI
分享收藏
查看更多内容