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[21]Xu Qifa，Fan Zhenhua,Jia Weiyin，Jiang Cuixia.，Fault detection of wind turbines via multivariate process monitoring based on vine copulas.[J:Renewable Energy,2020,161):939-955.
[22]Xu Qifa，Lu Shixiang,Zhai Zhongping,，Jiang Cuixia，Adaptive fault detection in wind turbine via RF and CUSUM.[J:IET Renewable Power Generation,2020,14(10):1789-1796.
[23]Xu Qifa，Lu Shixiang,Jia Weiyin，Jiang Cuixia，Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning.[J:Journal of Intelligent Manufacturing,2020,31(6):1467–1481.
[24]Xu Qifa，Chen Lu,Jiang Cuixia,Yu Keming，Mixed data sampling expectile regression with applications to measuring financial risk.[J:Economic Modelling,2020,91):469-486.
[25]Xu Qifa，Wang Liukai,Jiang Cuixia,Liu Yezheng，A novel (U)MIDAS-SVR model with multi-source market sentiment for forecasting stock returns.[J:Neural Computing and Applications,2020,32(10):5875-5888.
[26]许启发，卓杏轩，蒋翠侠，反向有约束混频数据模型的市场化利率预测.[J:管理科学学报,2019,22(10):55-71.
[27]许启发，王侠英,蒋翠侠,李辉艳，基于D-vine copula-分位数回归的组合投资决策.[J:系统工程学报,2019,34(1):69-81.
[28]Xu Qifa，Li Mengting,Jiang Cuixia,He Yaoyao，Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach.[J:Physica A: Statistical Mechanics and its Applications,2019,534):122173.
[29]Xu Qifa，Fan Zhenhua,Jia Weiyin,Jiang Cuixia，Quantile regression neural network-based fault detection scheme for wind turbines with application to monitoring a bearing.[J:Wind Energy,2019,22(10):1390-1401.
[30]Xu Qifa，Wang Liukai,Jiang Cuixia,Zhang Xin，A novel UMIDAS-SVQR model with mixed frequency investor sentiment for predicting stock market volatility.[J:Expert Systems with Applications,2019,132):12-27.
total124 3/13
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