姓 名：冯兴东
职 称：教授
研究方向：数据降维、稳健估计、分位数回归及其应用、分布式统计计算
教授课程：数理统计、应用生存分析、高等生存分析、计算统计、分布式统计计算、数据处理与可视化
个人主页：ddgene.github.io
研究项目
序号 | 项目名称 | 项目编号 | 项目来源 | 起止时间 | 项目经费 |
1 | 基于分位数回归的强化学习 | 12371270 | 国家自然科学基金面上项目 | 2024-2027年 | 43.5万 |
2 | 基于半参数分位数回归模型的时空数据分析 | 11971292 | 国家自然科学基金面上项目 | 2020-2023年 | 49万 |
3 | 分位数回归过程的估计及其应用 | 11571218 | 国家自然科学基金面上项目 | 2016-2019年 | 45万 |
4 | 矩阵的数学期望以及其他统计量的维数检验 | 11101254 | 国家自然科学基金青年项目 | 2012-2014年 | 22万 |
5 | 高维数据的变量选择及在金融和生物中的应用 | 13PJC048 | 上海市浦江人才计划团队项目 | 2013-2015年 | 30万 |
6 | 多智能体强化学习方法及其理论基础 | 上海市科学技术委员会课题 |
研究领域
分位数回归理论及其应用，分布式统计计算方法，矩阵数据降维理论与算法等
教育经历
2009年 美国伊利诺伊大学香槟分校统计学博士
2004年 加拿大约克大学统计学硕士
2002年 中国人民大学经济学硕士
1999年 南京大学理学士
工作经历
2015 至今 上海财经大学统计与管理学院 教授(Full professor with tenure)
2014-2015 上海财经大学统计与管理学院 副教授(Associate professor with tenure)
2012-2014 上海财经大学统计与管理学院 副教授(Associate professor without tenure)
2011-2012 上海财经大学统计与管理学院 助教授(Assistant professor)
2009-2011 美国国家统计科学研究所 博士后(Postdoc)
研究成果
Methodology (Journal) |
Wang, C., Li, T., Zhang, X., Feng, X., and He, X. (2024+). Communication-efficient nonparametric quantile regression via random features. Journal of Computational and Graphical Statistics to appear. |
Feng, X.*, Li, W., and Zhu, Q. (2024). Estimation and bootstrapping under spatiotemporal models with unobserved heterogeneity. Journal of Econometrics 238, 105559. |
Feng, X.*, Jiao, Y., Kang, L., Zhang, B. and Zhou, F. (2024+). Over-parameterized deep nonparametric regression for dependent data with its applications to reinforcement learning. Journal of Machine Learning Research 24（383),1-40 |
He,X., Ge,Y., and Feng, X.* (2023). Structure learning via unstructured kernel-based M-estimation. Electronic Journal of Statistics 17, 2386-2415. |
Feng,X., Li,W., and Zhu,Q. (2023). Spatial-temporal model with heterogeneous random effects.Statistica Sinica 33, 2613-2641. |
Yu,A.,Zhong,Y.,Feng, X.*, and Wei, Y. (2023). Quantile regression for nonignorable missing data with its application of analyzing electronic medical records. Biometrics 79, 2036-2049. |
Liu,Y.,Feng,X.*(2023).Clustering ambulatory missing data with applications to hypertension diagnostics (in Chinese). Journal of Applied Statistics and Management 42, 218-228. |
Feng,X.*, Liu, Q., and Wang, C. (2023). A lack-of-fit test for quantile regression process models.Statistics and Probability Letters 192, 109680. |
Li,X.,Feng,X.,and Liu,X. (2022). Heritability estimation for a linear combination of phenotypes via ridge regression.Bioinformatics 38,4687-4696. |
Cheng,C.,Feng,X.,Li,X.,and Wu,M.(2022). Robust analysis of cancer heterogeneity for high-dimensional data.Statistics in Medicine 41,5448-5462. |
Zhang,L.,Zhu,Z.,Feng, X.,and He, Y.* (2022).Shrinkage quantile regression estimation for panel data models with multiple structural breaks.Canadian Journal of Statistics 50,820-859. |
Liu,Q.,Feng,X.*(2022). Specification test of polynomials under partially linear additive quantile regression(in Chinese).Journal of Applied Statistics and Management 41, 294-308. |
Zhang,S.and Feng,X.*(2022). Distributed identification of heterogeneous treatment effects.Computational Statistics37, 57-89. Online Link |
Cheng,C.,Feng, X.*, Huang, J., Jiao, Y., and Zhang, S. (2022).ℓ0-regularized high-dimensional accelerated failure time model.Computational Statistics and Data Analysis 170, 107430.Online Link |
Cheng, C.,Feng, X., Huang, J. and Liu, X. (2022). Regularized projection score estimation of treatment effects in high-dimensional quantile regression.Statistica Sinica 32, 23-41. |
Dong,C.,Ma,S.,Zhu, L.,Feng, X.*(2021). Estimation and inference for non-crossing multiple-index quantile regression(in Chinese).SCIENTIA SINICA Mathematica 51,631-658. |
Liu,X.,Zheng,S.and Feng,X.*(2020).Estimation of error variance via ridge regression.Biometrika 107, 481-488. |
Dong,C.,Li,G.and Feng,X.*(2019). Lack-of-fit tests for quantile regression models.Journal of the Royal Statistical Society B 81, 629-648. |
Wang, H.,Feng, X.* and Dong,C.(2019). Copula-based quantile regression forlongitudinal data.Statistica Sinica 29, 245-264. |
Wu,M.,Zhu,L.,and Feng,X.*(2018). Network-based feature screening with applications to genome data.The Annals of Applied Statistics 12, 1250-1270. |
Feng,X.and He,X.(2017).Robust low-rank data matrix approximations.SCIENCE CHINA Mathematics 60,189-200. |
Feng,X.and Zhu,L.(2016). Estimation and testing of varying coefficients in quantile regression.Journal of the American Statistical Association 111, 266-274. |
Yi,Y.,Feng, X.,and Huang,Z.(2014). Estimation of extreme value-at-risk: an EVT approach for quantile GARCH model.Economics Letters 124, 378-381. |
Feng, X., Sedransk, N., and Xia, J.Q. (2014). Calibration using constrained smoothing with applications to mass spectrometry data. Biometrics 70, 398-408. |
Feng,X.,Feng,Y.,Chen,Y.and Small,D.S.(2014). Randomization inference for the trimmed mean of effects attributable to treatment.Statistica Sinica 24,773-797. |
Feng,X.and He,X.(2014).Statistical inference based on robust low-rank data matrix approximation. The Annals of Statistics 42, 190-210. |
Wang,H. and Feng, X.* (2012). Multiple imputation for M regression with censored covariates. Journal of the American Statistical Association 107, 194-204. |
Feng,X., He,X.and Hu,J. (2011). Wild bootstrap for quantile regression.Biometrika 98, 995-999. |
Feng,X.and He,X. (2009).Inference on low-rank data matrices with applications to microarray data. The Annals of Applied Statistics 3, 1634--1654. |
Wang,X., Liang, D,Feng, X.and Ye,L.(2007). A derivative free optimization algorithm based on conditional moments. Journal of Mathematical Analysis and Applications 331, 1337--1360. |
Note: '*'is used to refer to the corresponding author. |
Methodology (Conference) |
Feng, X., He, X., Wang, C., Wang, C. and Zhang, J. (2023). Towards a unified analysis of kernel-based methods under covariate shift.Neural Information Processing Systems 2023, New Orleans, USA. |
Zhou,F., Wang, J., and Feng, X.* (2020). Non-crossing quantile regression for deep reinforcement learning. Neural Information Processing Systems 2020, Vancouver, Canada. |
Wu,S., Feng, X., and Zhou, F. (2020). Metric learning by similarity network for deep semi-supervised learning.14th International FLINS Conference on Robotics and Artificial Intelligence (FLINS/ISKE2020), Cologne, Germany. |
Interdisciplinary Studies |
Abbatiello,S., Mani, D., Schilling, B., Maclean, B., Zimmerman, L.,Feng,X.etc.(2013). Design,Implementation, and Multi-Site Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in LC-MRM-MS.Molecular & Cellular Proteomics 12,2623--2639. |
Xia,J.Q.,Sedransk, N. and Feng, X.(2011). Variance component analysis of a multi-site study aiming at multiple reaction monitoring measurements of peptides in human plasma. Public Library of Science One 6, e14590. |
Broglio,S., Schnebel, B., Sosnoff, J., Shin, S. Feng, X., He, X. and Zimmerman, J. (2010). The biomechanical properties of concussions in high school football. Medicine and Science in Sports and Exercise 42, 2064--2071. |
Feng, X., Huang, S., Shou, J., Liao, B., Yingling, J. M., Ye, X., Lin, X., Gelbert, L. M., Su, E. W., Onyia, J. E. and Li, S. (2007). Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data. Genomics Proteomics and Bioinformatics 5, 15 -- 24. |
Xia,Y., Campen, A., Rigsby, D. , Guo, Y., Feng, X., Su, E.W., Dalakal, M.and Li, S. (2007). A Microarray Gene Expression Database for Primary Human Disease Tissues. Molecular Diagnosis and Therapy 11, 145--149. |
奖励，荣誉
社会工作
IMS、ASA 、ICSA会员，JASA, JRSSB, Annals of Statistics, Statistica Sinica, Bernoulli, JSPI等杂志审稿人
中国现场统计研究会理事（Elected Memeber of Chinese Association for Applied Statistics）
Elected Member of the International Statistical Institute
全国统计教材编审委员会第七届委员会专业委员（数据科学与大数据技术应用组）
学术报告（2008年以来）
THE 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting, June 2018, Singapore, Invited Talk, Lack-of-fit tests for quantile regression models.
2017 1st International Conference on Econometrics and Statistics, June 2017, Hong Kong, Invited Talk, Lack-of-fit tests for quantile regression models.
2016 Applied Statistics Symposium of International Chinese Statistical Association, June 2016, Atlanta, Georgia, USA, Invited Talk, Non-crossing quantile surfaces.
Joint Statistical Meetings 2015, August 2015, Seattle, Washington, USA, Topic Contributed Talk, Copula-based quantile regression for longitudinal data.
第十届全国概率统计会议概率统计学会，2015年10月，山东大学，中会报告，Estimation and testing of varying coefficients in quantile regression.
The 59th World Statistics Congress, August 2013, Hong Kong, Invited Talk, Estimation and testing of varying coefficients in quantile regression.
The 9th ICSA international conference, December 2013, Hong Kong, Invited Talk, Efficient estimation of treatment effects in high-dimensional quantile regression.
IMS-APRM 2012, July 2012, Tsukuba, Lbaraki, Japan, Invited Talk, Wild bootstrap for M-estimators of linear regression.
ENAR 2011, March 2011, Miami, Florida, USA, March 2011, Contributed Talk, Calibration using constrained smoothing with applications to mass spectrometry data.
Joint Statistical Meetings 2011, August 2011, Miami Beach, Florida, USA, Contributed Talk, Wild bootstrap for quantile regression.
Joint Statistical Meetings 2010, August 2010, Vancouver, BC, Canada, Topic Contributed Talk, Constrained smoothing of scatterplots with applications to mass spectrometry data.
Joint Statistical Meetings 2009, August 2009, Washington DC, USA, Topic Contributed Talk, Estimation of variance of least absolute deviation regression estimator with applications to protein lysate arrays.
