卢芳

发布人:日期:2023年03月08日 15:50浏览数:

卢芳,女,博士,讲师

教育背景:

2007.09-2011.07   周口师范学院   数学与应用数学系 学士

2011.09-2016.07   重庆大学yl23455永利 运筹学与控制论专业 硕博连读

工作经历:

2017.01-至今   yl23455永利官方 数学系 讲师

教学育人:

本科课程:运筹学、高等数学

科学研究:

研究领域:最优性理论与方法、KKT条件、随机优化

科研项目:

  1. 国家自然科学基金青年项目,11801169,随机向量变分不等式问题的解及其收敛性分析,19万,2019.01-2021.12,主持

  2. 湖南省自然科学基金青年项目,2019JJ50378,随机向量变分不等式问题的解及其收敛性分析,5万,2019.01-2021.12,主持

代表性论文(*为通讯作者):

  1. Yang Jing, Tian Guoliang, Lu Fang* and Lu Xuewen (2020). Single-index modal regression via outer product gradients. Computational Statistics & Data Analysis, 144: 106867.

  2. Yang Jing, Lu Fang* and Lu Xuewen (2020). Robust check loss-based inference of semiparametric models and its application in environmental data. Journal of Computational and Applied Mathematics, 365, 112267

  3. Yang Jing*, Lu Fang and Yang Hu (2019). Local Walsh-average based estimation and variable selection for the single-index models. Science China Mathematics, 62, 1977-1996.

  4. Yang Jing*, Lu Fang, Tian Guoliang, Lu Xuewen and Yang Hu (2019). Robust variable selection of varying coefficient partially nonlinear model based on quantile regression. Statistics and Its Interface, 12, 397-413.

  5. Yang Jing*, Lu Fang and Yang Hu (2018). Statistical inference on asymptotic properties of two estimators for the partially linear single-index models. Statistics, 52: 1193-121.

  6. Yang Jing*, Lu Fang and Yang Hu (2018). Quantile regression for robust inference on varying coefficient partially nonlinear models. Journal of the Korean Statistical Society, 47: 172-184.

  7. Yang Jing*, Lu Fang and Yang Hu (2017). Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models. Statistics, 51: 1179-1199.

  8. Lu Fang, Shengjie Li* and Shengkun Zhu (2016). Exact penalization and strong Karush-Kuhn-Tucker conditions for nonsmooth multiobjective optimization problems. Pacific Journal of Optimization, 12(2): 245-261.

  9. Lu Fang and Shengjie Li* (2016). Convexificators and Strong Karush-Kuhn-Tucker Conditions for Nonsmooth Multiobjective Optimization Problems. Pacific Journal of Optimization, 12(4): 699-715.

  10. Lu Fang, Shengjie Li* and Jing Yang (2015). Convergence Analysis of Weighted Expected Residual Method for Nonlinear Stochastic Variational Inequality Problems. Mathematical Methods of Operations Research, 82(2): 229-242.

  11. Lu Fang and Shengjie Li* (2015). Method of Weighted Expected Residual for Solving Stochastic Variational Inequality Problems. Applied Mathematics and Computation, 269: 651-663.

  12. Lu Fang and Chunrong Chen* (2014). Newton-Like Methods for Solving Vector Optimization Problems. Applicable Analysis, 93(8): 1567-1586

联系方式:lf@hunnu.edu.cn

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