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An extreme bias-penalized forecast combination approach to commodity price forecasting 期刊论文
INFORMATION SCIENCES, 2022, 卷号: 615, 页码: 774-793
作者:  Zhang, Yifei;  Wang, Jue;  Yu, Lean;  Wang, Shouyang
收藏  |  浏览/下载：67/0  |  提交时间：2023/02/07
Forecast combination  Elastic net  Extreme bias  Weight-sparsity  Artificial bee colony algorithm  
A multi-granularity heterogeneous combination approach to crude oil price forecasting 期刊论文
ENERGY ECONOMICS, 2020, 卷号: 91, 页码: 9
作者:  Wang, Jue;  Zhou, Hao;  Hong, Tao;  Li, Xiang;  Wang, Shouyang
收藏  |  浏览/下载：145/0  |  提交时间：2021/01/14
Crude oil Price  Forecast combination  Feature selection  Multi-granularity  Artificial bee colony  
A posteriori error analysis of nonconforming finite element methods for convection-diffusion problems 期刊论文
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2017, 卷号: 321, 页码: 416-426
作者:  Zhang, Bei;  Chen, Shaochun;  Zhao, Jikun;  Mao, Shipeng
收藏  |  浏览/下载：126/0  |  提交时间：2018/07/30
A posteriori error estimates  Semi-robustness and robustness  Nonconforming quadrilateral finite elements  Convection-diffusion problem  
Power-transformed linear quantile regression estimation for censored competing risks data 期刊论文
Statistics and Its Interface, 2017, 卷号: 10, 期号: 2, 页码: 239-254
作者:  Fan, Caiyun;  Zhang, Feipeng;  Zhou, Yong
收藏  |  浏览/下载：186/0  |  提交时间：2018/07/30
Box-Cox transformation  Censored data  Competing risks  Quantile regression  
Charged Substrate and Product Together Contribute Like a Nonreactive Species to the Overall Electrostatic Steering in Diffusion-Reaction Processes 期刊论文
JOURNAL OF PHYSICAL CHEMISTRY B, 2016, 卷号: 120, 期号: 33, 页码: 8147-8153
作者:  Xu, Jingjie;  Xie, Yan;  Lu, Benzhuo;  Zhang, Linbo
收藏  |  浏览/下载：118/0  |  提交时间：2018/07/30
Power-transformed linear regression on quantile residual life for censored competing risks data 期刊论文
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2016, 卷号: 45, 期号: 20, 页码: 5884-5905
作者:  Fan, Caiyun;  Zhang, Feipeng;  Zhou, Yong
收藏  |  浏览/下载：112/0  |  提交时间：2018/07/30
Box-Cox transformation  Competing risks  Empirical process  Quantile residual life  62N01  62N02