陈雪蓉
- 讲师
- 职务 教授 博士生导师
Email:chenxuerong@swufe.edu.cn
地址：四川省成都市温江区柳台大道555号
研究方向
机器学习、大数据分析、隐私保护、生物统计、半参数非参数建模、复杂数据分析
教师介绍：
陈雪蓉，西南财经大学“光华杰出学者计划”青年杰出教授、博士生导师，国家级青年人才计划入选者，省级高层次人才入选者。中科院数学与系统科学研究院博士（联合培养），美国密苏里大学统计系、乔治城大学生物统计博士后，美国密歇根大学、香港城市大学、香港大学访问学者。论文发表于JASA, Biometrics, Journal of Business and Economic Statistic等统计学、生物统计学、计量经济学权威期刊。主持国家自然科学基金面上项目2项、青年项目、国家自然科学基金重点项目子课题、国家重点研发计划课题子课题各1项。曾荣获教育部“第八届高等学校科学研究优秀成果奖青年成果奖”。
教授课程：
本科生：多元统计分析、分类数据分析、非参数统计
硕士生：广义线性模型、非参数统计、隐私保护、生存分析
热忱欢迎有志从事统计学、数据科学行业,数学基础扎实、编程能力强（熟练掌握R、Python、Matlab或C之一）、具有团队合作精神及吃苦耐劳精神的本科生、硕士生及博士生加入我的团队。我将资助博士生（每个博士生不少于两次）及优秀的硕士生参加学术会议、专业培训及暑期学校。
当前主要研究兴趣：
2016.4---2022.12 西南财经大学 统计研究中心 副教授
2016.12-2017.8 密歇根大学 生物统计系 访问助理教授
2016.7---2016.8 香港大学 统计与精算系 访问学者
2014.9---2016.4 西南财经大学 统计研究中心 讲师
2013.8---2014.8 乔治城大学 生物统计系 博士后
2012.9---2013.8 密苏里大学 统计系 博士后
2012.3---2012.5 香港城市大学 管理科学系 访问学者
2011.9---2011.11 香港城市大学 管理科学系 访问学者
2011.2---2011.6 香港城市大学 管理科学系 访问学者
陈雪蓉，西南财经大学“光华杰出学者计划”青年杰出教授、博士生导师，国家级青年人才计划入选者，省级高层次人才入选者。中科院数学与系统科学研究院博士（联合培养），美国密苏里大学统计系、乔治城大学生物统计博士后，美国密歇根大学、香港城市大学、香港大学访问学者。论文发表于JASA, Biometrics, Journal of Business and Economic Statistic等统计学、生物统计学、计量经济学权威期刊。主持国家自然科学基金面上项目2项、青年项目、国家自然科学基金重点项目子课题、国家重点研发计划课题子课题各1项。曾荣获教育部“第八届高等学校科学研究优秀成果奖青年成果奖”。
教授课程：
本科生：多元统计分析、分类数据分析、非参数统计
硕士生：广义线性模型、非参数统计、隐私保护、生存分析
博士生：大样本理论、数据科学基础、缺失数据
热忱欢迎有志从事统计学、数据科学行业,数学基础扎实、编程能力强（熟练掌握R、Python、Matlab或C之一）、具有团队合作精神及吃苦耐劳精神的本科生、硕士生及博士生加入我的团队。我将资助博士生（每个博士生不少于两次）及优秀的硕士生参加学术会议、专业培训及暑期学校。
当前主要研究兴趣：
机器学习、大数据分析、隐私保护、生物统计、半参数非参数建模、复杂数据分析。
工作经历：
2023.1---至今 西南财经大学 统计研究中心 教授2016.4---2022.12 西南财经大学 统计研究中心 副教授
2016.12-2017.8 密歇根大学 生物统计系 访问助理教授
2016.7---2016.8 香港大学 统计与精算系 访问学者
2014.9---2016.4 西南财经大学 统计研究中心 讲师
2013.8---2014.8 乔治城大学 生物统计系 博士后
2012.9---2013.8 密苏里大学 统计系 博士后
2012.3---2012.5 香港城市大学 管理科学系 访问学者
2011.9---2011.11 香港城市大学 管理科学系 访问学者
2011.2---2011.6 香港城市大学 管理科学系 访问学者
荣誉：
教育部“第八届高等学校科学研究优秀成果奖（人文社会科学）青年成果奖”；
四川省现场统计学会第二届教学成果奖“特等奖”
已发表论文：
[30] Senlin Yuan, Xuerong Chen*, Yu Wu and Jianguo Sun, Integrative Quantile Regression Analysis of Heterogeneous Multisource Data with Privacy Preserving, Statistica Sinica, Online
[29] Zhang,P.,Chen,X*.and Sun,J.（2024）.Regression analysis of longitudinal data with random change point,Statistical Methods in Medical Research,33(4):634-646.
[28] Chen, X.*，Yuan，S.(2024).Renewable Quantile Regression with Heterogeneous Streaming Datasets,Journal of Computational and Graphical Statistics,33(4) 1185-1201.
[27] Chen, X.*, Ping, Y. and Sun, J.(2024).Efficient estimation of Cox model with random change point. Statistics in Medicine,43,1213-1226.
[26] Lan,W., Chen, X.*, Zou, T. and Tsai, C-L.(2022). Imputations for High Missing Rate Data in Covariates via Semi-supervised Learning Approach. Journal of Business and Economic Statistics, 40, 1282-1290.
[25] Chen, X., Leung, H. and Qin, J.(2022). Nonignorable missing data, single index propensity score and a profile synthetic distribution function method. Journal of Business and Economic Statistics , 40, 705-717.
[24] Chen, X.*, Diao, G. and Qin, J.(2020). Pseudo likelihood based estimation and testing of missing propensity function in nonignorable missing data problems. Scandinavia Journal of Statistics,47,1377-1400.
[23] Lee, J., Chen, X. and Lam, E.(2020). Testing for change-point in the covariate effects based on the Cox proportional hazards model. Statistics in Medicine, 39,1473–1488.
[22] Hong. G., Chen, X., Kang, J. and Li Y.(2020). The Lq-norm learning for ultrahigh-dimensional survival data: an integrative framework. Statistica Sinica, 30, 1213-1233.
[21] Hu, N.,Chen, X.* and Sun, J.(2020). Semiparametric Analysis of Short-Term and Long-Term Hazard Ratio Models with Length-Biased and Right-Censored Data. Statistica Sinica, 30, 487-509.
[20] Bai, F.,Chen, X., Chen, Y. and Huang, T.(2019). A General Quantile Residual Life Model forLength-Biased Right-Censored Data. Scandinavia Journal of Statistics,46,1191-1205.
[19] Chen, X.*, Chen, Y., Wan, A. and Zhou, Y. (2018). On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data. Scandinavian Journal of Statistics,46,361-388.
[18] Chen, X., Li, H, Lin, H, Liang, H.(2019).Functional response regression analysis. Journal of Multivariate Analysis, 169, 218-233.
[17] Hong,G.,Chen, X., Christiani, D. and Li Y.(2017). Integrated Powered Density: Screening Ultrahigh-Dimensional Covariates with Survival Outcomes. Biometrics, 74, 421-429.
[16] Chen, X. and Hu, T and Sun, J(2017). Sieve Maximum Likelihood Estimation for the Proportional Hazards Model under Informative Censoring. Computational Statistics and Data Analysis,112,224-234.
[15] Chen, X*., Hu,N., Sun,J. (2017). Smooth composite likelihood analysis of length-biased and right-censored data with the AFT model. Statistica Sinica, 27,229-242.
[14] Fang, H.,Chen, X., Grant, S., Pei, X. and Tan, M(2015). Experimental design and statistical analysis for three drugs combination studies. Statistical Methods in Medical Research, 26,1261-1280.
[13] Chen,X.*,Liu,Y.,Sun,J. and Zhou, Y.(2016). Semiparametric quantile regression analysis of right-censored and length-biased failure time data with partially linear varying effects. Scandinavian Journal of Statistics, 43, 921-938.
[12] Chen,X.*, Tang,N. and Zhou,Y.(2016). Quantile regression of longitudinal data with informative observation times. Journal of Multivariate Analysis, 144, 176-188.
[11] Chen, X., Wan,A. and Zhou, Y.(2015) Efficient quantile regression analysis with missing observations. Journal of the American statistical Association, 110, 723-741.
[10] Hu,N., Chen, X.* and Sun, J(2015). Regression analysis of length-biased and right-censored failure time data with missing covariates. Scandinavian Journal of Statistics, 42, 438-452
[9] Chen, X.*, Sun, J. and Liu, L.(2015) Semiparametric partial Linear quantile regression of longitudinal data with time varying coefficients and informative observation times. Statistics Sinica, 25, 1437-1458.
[8] Chen,X., Wan, A. and Zhou, Y.(2014). A quantile varying-coefficient regression approach to length-biased data modeling. Electronic Journal of Statistics, 8, 2514-2540.
[7] Chen, X. and Zhou, Y.(2012) Quantile Regression for Right-Censored and Length-Biased Data. Acta Mathematicae Apllicatae Sinica(English Series) , 28, 443-462.
[6] Zhang,F, Chen, X. and Zhou, Y. Proportional Hazards Models with Varying Coefficients for Right-Censored Length-Biased Data. Lifetime Data Analysis ,20, 132-157.
[5] Ma, Y., Wan, A., Chen,X. and Zhou, Y. (2013). On estimation and inference in a partially linear hazard model with varying coefficients. Annals of the Institute of Statistical Mathematics, 66, 931-960.
[4] 赵晓玲，陈雪蓉，周勇.(2012) 基于非参估计的VaR和ES方法的应用研究.《数理统计与管理》,3,381-388.
[3] Li,Y., Chen, X. and Zhao, L. (2009). Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales. Neurocomputing, 72, 1621-1630.
[2] Li,Y., Zhao, L. and Chen, X. (2010). Positive periodic solutions of functional differential equations with impulse on time scales. Journal of Applied Mathematics and Computing, 34, 495-510.
[1] Li,Y., Zhao, L. and Chen, X. (2012). Existence of periodic solutions for neutral type cellular neural networks with delays. Applied Mathematical Modelling, 36, 1173-1183.
[30] Senlin Yuan, Xuerong Chen*, Yu Wu and Jianguo Sun, Integrative Quantile Regression Analysis of Heterogeneous Multisource Data with Privacy Preserving, Statistica Sinica, Online
[29] Zhang,P.,Chen,X*.and Sun,J.（2024）.Regression analysis of longitudinal data with random change point,Statistical Methods in Medical Research,33(4):634-646.
[28] Chen, X.*，Yuan，S.(2024).Renewable Quantile Regression with Heterogeneous Streaming Datasets,Journal of Computational and Graphical Statistics,33(4) 1185-1201.
[27] Chen, X.*, Ping, Y. and Sun, J.(2024).Efficient estimation of Cox model with random change point. Statistics in Medicine,43,1213-1226.
[26] Lan,W., Chen, X.*, Zou, T. and Tsai, C-L.(2022). Imputations for High Missing Rate Data in Covariates via Semi-supervised Learning Approach. Journal of Business and Economic Statistics, 40, 1282-1290.
[25] Chen, X., Leung, H. and Qin, J.(2022). Nonignorable missing data, single index propensity score and a profile synthetic distribution function method. Journal of Business and Economic Statistics , 40, 705-717.
[24] Chen, X.*, Diao, G. and Qin, J.(2020). Pseudo likelihood based estimation and testing of missing propensity function in nonignorable missing data problems. Scandinavia Journal of Statistics,47,1377-1400.
[23] Lee, J., Chen, X. and Lam, E.(2020). Testing for change-point in the covariate effects based on the Cox proportional hazards model. Statistics in Medicine, 39,1473–1488.
[22] Hong. G., Chen, X., Kang, J. and Li Y.(2020). The Lq-norm learning for ultrahigh-dimensional survival data: an integrative framework. Statistica Sinica, 30, 1213-1233.
[21] Hu, N.,Chen, X.* and Sun, J.(2020). Semiparametric Analysis of Short-Term and Long-Term Hazard Ratio Models with Length-Biased and Right-Censored Data. Statistica Sinica, 30, 487-509.
[20] Bai, F.,Chen, X., Chen, Y. and Huang, T.(2019). A General Quantile Residual Life Model forLength-Biased Right-Censored Data. Scandinavia Journal of Statistics,46,1191-1205.
[19] Chen, X.*, Chen, Y., Wan, A. and Zhou, Y. (2018). On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data. Scandinavian Journal of Statistics,46,361-388.
[18] Chen, X., Li, H, Lin, H, Liang, H.(2019).Functional response regression analysis. Journal of Multivariate Analysis, 169, 218-233.
[17] Hong,G.,Chen, X., Christiani, D. and Li Y.(2017). Integrated Powered Density: Screening Ultrahigh-Dimensional Covariates with Survival Outcomes. Biometrics, 74, 421-429.
[16] Chen, X. and Hu, T and Sun, J(2017). Sieve Maximum Likelihood Estimation for the Proportional Hazards Model under Informative Censoring. Computational Statistics and Data Analysis,112,224-234.
[15] Chen, X*., Hu,N., Sun,J. (2017). Smooth composite likelihood analysis of length-biased and right-censored data with the AFT model. Statistica Sinica, 27,229-242.
[14] Fang, H.,Chen, X., Grant, S., Pei, X. and Tan, M(2015). Experimental design and statistical analysis for three drugs combination studies. Statistical Methods in Medical Research, 26,1261-1280.
[13] Chen,X.*,Liu,Y.,Sun,J. and Zhou, Y.(2016). Semiparametric quantile regression analysis of right-censored and length-biased failure time data with partially linear varying effects. Scandinavian Journal of Statistics, 43, 921-938.
[12] Chen,X.*, Tang,N. and Zhou,Y.(2016). Quantile regression of longitudinal data with informative observation times. Journal of Multivariate Analysis, 144, 176-188.
[11] Chen, X., Wan,A. and Zhou, Y.(2015) Efficient quantile regression analysis with missing observations. Journal of the American statistical Association, 110, 723-741.
[10] Hu,N., Chen, X.* and Sun, J(2015). Regression analysis of length-biased and right-censored failure time data with missing covariates. Scandinavian Journal of Statistics, 42, 438-452
[9] Chen, X.*, Sun, J. and Liu, L.(2015) Semiparametric partial Linear quantile regression of longitudinal data with time varying coefficients and informative observation times. Statistics Sinica, 25, 1437-1458.
[8] Chen,X., Wan, A. and Zhou, Y.(2014). A quantile varying-coefficient regression approach to length-biased data modeling. Electronic Journal of Statistics, 8, 2514-2540.
[7] Chen, X. and Zhou, Y.(2012) Quantile Regression for Right-Censored and Length-Biased Data. Acta Mathematicae Apllicatae Sinica(English Series) , 28, 443-462.
[6] Zhang,F, Chen, X. and Zhou, Y. Proportional Hazards Models with Varying Coefficients for Right-Censored Length-Biased Data. Lifetime Data Analysis ,20, 132-157.
[5] Ma, Y., Wan, A., Chen,X. and Zhou, Y. (2013). On estimation and inference in a partially linear hazard model with varying coefficients. Annals of the Institute of Statistical Mathematics, 66, 931-960.
[4] 赵晓玲，陈雪蓉，周勇.(2012) 基于非参估计的VaR和ES方法的应用研究.《数理统计与管理》,3,381-388.
[3] Li,Y., Chen, X. and Zhao, L. (2009). Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales. Neurocomputing, 72, 1621-1630.
[2] Li,Y., Zhao, L. and Chen, X. (2010). Positive periodic solutions of functional differential equations with impulse on time scales. Journal of Applied Mathematics and Computing, 34, 495-510.
[1] Li,Y., Zhao, L. and Chen, X. (2012). Existence of periodic solutions for neutral type cellular neural networks with delays. Applied Mathematical Modelling, 36, 1173-1183.
科研项目：
[6] 主持国家自然科学基金面上项目：流数据可更新推断与预测研究，No. 12371296, 2024-2027.[5] 主持国家重点研发计划课题“分布式统计学习理论与方法”子课题一项
[4] 主持国家自然科学基金面上项目：复杂数据下结构突变模型的统计推断及应用，No. 11871402, 2019.01-2022.12.
[3] 主持国家自然科学基金青年项目：两类不完全数据下基于秩以及非光滑估计方程的统计推断及其应用，No.1150146 1,2016.1-2018.12
[2] 主持国家自然科学基金重点项目：“半参数集成回归推断”子项目，项目编号：11931014，执行期限：2020.01-2024.12
[1] 参加国家自然科学基金面上项目：轨道数据的聚类分析，No.11571282，2016.1-2019.12
发明专利：
《通信高效的联邦矩阵分解推荐方法》
下列杂志审稿人：
Journal of the American statistical Association，Scandinavia Journal of Statistics，Biometrics，Journal of Multivariate Analysis，Journal of Nonparametric Statistics，StatisticsAmerican Journal of Biostatistics中国现场统计研究会经济与金融统计分会常务理事
中国现场统计研究会资源与环境统计分会副秘书长
中国现场统计研究会资源与环境统计分会常务理事
指导博士生：
2019.9-2024.6 袁森林
2020.9-2024.6 张鹏 平雅露
2022.9-至今 申博延
2023.9-至今 陈露
2024.9-至今 孙柯祎、赵京京、左永宝
2025.9-至今 杨昌富、唐立
指导硕士生：
2021.9-2023.6 陈怡、李浩
2021.9-2024.6 邱霁钊
2023.9-至今 马靖凯
2024.0 -至今 崔佳舒、杨利、李欣蔚、吴秀秀