教师队伍

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范新妍

职 称:


职 务:


电子邮箱: 20198102@ruc.edu.cn

教育经历

2009年09月-2013年06月 统计学专业 理学学位 武汉理工大学理学院

2013年09月-2019年06月 统计学专业 理学学位 厦门大学经济学院

工作经历

2019年07月-2021年8月 博士后 中国人民大学统计学院

2021年9月-至今 讲师 中国人民大学统计学院

基金项目

新教师启动金项目,2020----2022,主持

国家自然科学基金青年基金项目,2023--2025,主持

开设课程

复杂数据分析, 概率论,统计学,多元数据分析

研究方向

高维数据分析、网络结构分析,多源数据分析

论文成果

范新妍, 王仲君. 基于 CD4 细胞含量的 AIDS 病人免疫能力分析与疗效评估. 中国卫生统计, 2012, 29(6): 877-878。

方匡南, 范新妍, 马双鸽. 基于网络结构 Logistic 模型的企业信用风险预警 [J]. 统计研究, 2016, 33(4): 50-55.

朱建平, 谢邦昌, 骆翔宇, 范新妍, 曾武雄, 郑陈璐. 中国房地产网络舆情分析 [J]. 数理统计与管理, 2016, 35(4): 722-741.

范新妍, 方匡南, 郑陈璐, 张志远. 基于整合治愈率模型的信贷违约时点预测 [J]. 统计研究,2021,38(2):99-113.

方匡南, 李晶茂, 范新妍, 余乐安, 基于多源域知识迁移学习的小微企业信用评分 [J]. 系统工程理论与实践,2023, 43(5): 1320-1332.

Fan X, Liu M, Fang K, Huang Y, Ma S. Promoting structural effects of covariates in the cure rate model with penalization[J]. Statistical methods in medical research, 2017, 26(5): 2078-2092.

Fan X, Fang K, Zhang Q, Ma S. Integrative sparse principal component analysis of gene expression data[J]. Genetic epidemiology, 2017, 41(8): 844-865.

Fang K, Fan X, Zhang Q, Ma S. Integrative sparse principal component analysis[J]. Journal of Multivariate Analysis, 2018, 166: 1-16.

Fang K, Fan X, Lan W, Wang B. Nonparametric additive beta regression for fractional response with application to body fat data[J]. Annals of Operations Research, 2019, 276: 331-347.

Fan X, Fang K, Ma S, Wang S, Zhang Q. Assisted graphical model for gene expression data analysis[J]. Statistics in medicine, 2019, 38(13): 2364-2380.

Fan X, Fang K, Ma S, Zhang Q. Integrating approximate single factor graphical models[J]. Statistics in Medicine, 2020, 39(2): 146-155.

Fan X, Zhang Q, Ma S, Fang K. Conditional score matching for high-dimensional partial graphical models[J]. Computational Statistics and Data Analysis, 2021, 153:107066.

Liu M, Fan X, Ma S. A Linguistic Analysis of News Coverage of E-Healthcare in China with a Heterogeneous Graphical Model[J]. Entropy, 2022, 24(11): 1557.

Zhang J, Fan X, Li Y, Ma S. Heterogeneous graphical model for non‐negative and non‐Gaussian PM2. 5 data[J]. Journal of the Royal Statistical Society Series C, 2022, 71(5): 1303-1329.

Sun Y, Luo Z, Fan X. Robust structured heterogeneity analysis approach for high‐dimensional data[J]. Statistics in Medicine, 2022, 41(17): 3229-3259.

Luo Z, Yao X, Sun Y, Fan X. Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data[J]. Biometrical Journal, 2022, 64(6): 1109-1141.

Fan X, Lan W, Zou T, Tsai C-L. Covariance Model with General Linear Structure and Divergent Parameters[J]. Journal of Business & Economic Statistics, 2022,  In Press.

Fang K, Fan X, Ma S,  Zhang Q. Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data[J].  Journal of Statistical Computation and Simulation, 2022, 92(14): 3045-3065.

Fan X, Lan W, Zou T, Tsai C-L. Mutual Influence Regression Model[J]. Statistica Sinica, 2023, In Press.

Liu M, Fan X, Ma S. A quantitative linguistic analysis of a cancer online health community with a smooth latent space model[J].  Annals of Applied Statistics, 2023, In Press.

Wu Y, Lan W, Fan X,  Fang K.  Bipartite network influence analysis of a two-mode network[J]. Journal of Econometrics, 2024, 105562.

Fan X, Fang K, Pu D, Qin R.  Generalized latent space model for one-mode networks with awareness of two-mode networks[J]. Computational Statistics and Data Analysis, 2024, In Press.

Zhang J, Lan W, Fan X, Chen W. Maximum conditional alpha test for conditional multi-factor models[J]. Statistica Sinica, 2024, In Press.