个人信息：

兰伟，教授，博导

西南财经大学统计学院

电子邮箱：lanwei@haoyii.com

简历：

2013年7月于北京大学光华管理学院取得经济学博士学位，同年进入西南财经大学统计学院工作。现为西南财经大学统计学院教授，博士生导师，北京大学商务智能研究中心研究员。

主持项目：

• 2025.01—2027.12：国家自然科学基金优秀青年基金项目《金融风险管理中的计量经济学方法研究》

• 2024.01—2028.12：国家自然科学基金重点项目《空间/网络计量建模理论及其经济应用》（子项目负责人）

• 2022.12—2027.11：科技部重点研发计划《分布式统计学习理论与方法》（参与）

• 2022.01—2025.12：国家自然科学基金面上项目《大型协方差矩阵的结构化估计和检验》

• 2020.01—2024.12：国家自然科学基金重点项目《半参数集成回归推断》（子项目负责人）

• 2020.01—2024.12：国家自然科学基金重大项目子课题《时空数据建模与预测研究》（参与）

• 2016.01—2020.12：国家自然科学基金重点项目《大数据驱动的管理决策模型和算法》（子项目负责人）

• 2015.01—2017.12：国家青年自然科学基金《高维近似因子模型框架下的多重检验及其应用》

学术兼职：

中国青年统计学家协会副会长，四川省现场统计学会副理事长，全国工业统计学教学研究会常务理事，STAT副主编，Journal of the American Statistical Association、The Annals of Statistics、Journal of Business and Economic Statistics、Journal of Econometrics等国内外著名期刊匿名审稿人。

代表性论文：

• Zhang, D., Feng, L., Wu, Y., Lan, W., & Zhou, J. (2025+). Temporal network influence model with application to the COVID-19 population flow network. Annals of Applied Statistics, in press.

• Fan, X., Fang, K., Lan, W., & Tsai, C. L. (2025+). Network varying coefficient model. Journal of the American Statistical Association, in press.

• Zou, T., Lan, W., Li, R., & Tsai, C. L. (2025+). Fixed and random covariance regression analyses. Annals of Statistics, in press.

• Ma, Y., Lan, W., Leng, C., Li, T., & Wang, H. (2025+). Supervised centrality via sparse network influence regression: An application to the 2021 Henan floods. Annals of Applied Statistics, in press.

• Chen, Y., Fang, K., Lan, W., Tsai, C. L., & Zhang, Q. (2025). Community influence analysis in social networks. Computational Statistics & Data Analysis, 202, 108037.

• Pu, D., Fang, K., Lan, W., Yu, J., & Zhang, Q. (2025). Reduced rank spatio-temporal models. Journal of Business & Economic Statistics, 43, 98-109.

• Pu, D., Fang, K., Lan, W., Yu, J., & Zhang, Q. (2024). Multivariate spatiotemporal models with low rank coefficient matrix. Journal of Econometrics, 246, 105897.

• Zhang, J., Lan, W., Fan, X., & Chen, W. (2023+). Maximum Conditional Alpha Test for Conditional Multi-Factor Models. Statistical Sinica, In Press.

• Wu, Y., Lan, W., Fan, X., & Fang, K. (2024). Bipartite network influence analysis of a two-mode network. Journal of Econometrics, 239, 105562.

• Lan, W., Lei, B., Feng, L., & Tsai, C. L. (2024). Maximum-subsampling test of equal predictive ability. Journal of Business & Economic Statistics, 42, 1344-1355.

• Fan, X., Lan, W., Zou, T., & Tsai, C. L. (2024). Mutual influence regression model. Statistica Sinica, 34, 1723-1743.

• Fan, X., Lan, W., Zou, T., & Tsai, C. L. (2024). Covariance model with general linear structure and divergent parameters. Journal of Business & Economic Statistics, 42, 36-48.

• Fang, K., Lan, W., Pu, D., & Zhang, Q. (2024). Spatial autoregressive models with generalized spatial disturbances. Statistica Sinica, 34, 725-745.

• Lei, B., Lan, W., Fang, N., & Zhou, J. (2023). Polynomial network autoregressive models with divergent order. Science China Mathematics, 66, 1073-1086.

• Zhou, J., Lan, W., & Wang, H. (2022). Asymptotic covariance estimation by Gaussian random perturbation. Computational Statistics & Data Analysis, 171, 107459.

• Zhang, R., Zhou, J., Lan, W., & Wang, H. (2022). A case study on the shareholder network effect of stock market data: An SARMA approach. Science China Mathematics, 65, 2219-2242.

• Xiao, B., Lei, B., Lan, W., & Guo, B. (2022). A blockwise network autoregressive model with application for fraud detection. Annals of the Institute of Statistical Mathematics, 74, 1043-1065.

• Zou, T., Lan, W., Li, R., & Tsai, C. L. (2022). Inferences on covariance-mean regression. Journal of Econometrics, 230, 318-338.

• Wu, Y., Lan, W., Zou, T., & Tsai, C. L. (2022). Inward and outward network influence analysis. Journal of Business & Economic Statistics, 40, 1617-1628.

• Lan, W., Chen, X., Zou, T., & Tsai, C. L. (2022). Imputations for high missing rate data in covariates via semi-supervised learning approach. Journal of Business & Economic Statistics, 40, 1282-1290.

• Feng, L., Lan, W., Liu, B., & Ma, Y. (2022). High-dimensional test for alpha in linear factor pricing models with sparse alternatives. Journal of Econometrics, 229, 152-175.

• Zou, T., Luo, R., Lan, W., & Tsai, C. L. (2021). Network influence analysis. Statistica Sinica, 31, 1727-1748.

• Lin, H., Liu, W., & Lan, W. (2021). Regression analysis with individual-specific patterns of missing covariates. Journal of Business & Economic Statistics, 39, 179-188.

• Ma, S., Lan, W., Su, L., & Tsai, C. L. (2020). Testing alpha in conditional time-varying factor models with high dimensional assets. Journal of Business & Economic Statistics, 38, 214-227.

• Ma, Y., Lan, W., Zhou, F., & Wang, H. (2020). Approximate least squares estimation for spatial autoregressive models with covariates. Computational Statistics & Data Analysis, 143, 106833.

• Zou, T., Luo, R., Lan, W., & Tsai, C. L. (2020). Covariance Regression Model for Non-Normal Data. In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (Chapter 113). Ed. Lee, C. F. and Lee, J. World Scientific: Singapore.

• Kuang, K., Fan, X., Lan, W., & Wang, B. (2019). Nonparametric additive beta regression for fractional response with application to body fat data. Annals of Operations Research, 276, 331-347.

• Luo, R., Liu, Y., & Lan, W. (2019). A penalized expected risk criterion for portfolio selection. China Finance Review International, 3, 386-400.

• Lan, W., & Du, L. (2019). A factor-adjusted multiple testing procedure with application to mutual fund selection. Journal of Business & Economic Statistics, 37, 147-157.

• Fang, F., Lan, W., Tong, J., & Shao, J. (2019). Model averaging for prediction with fragmentary data. Journal of Business & Economic Statistics, 37, 517-527.

• Huang, D., Lan, W., Zhang, H., & Wang, H. (2019). Least squares estimation for social autocorrelation in large-scale networks. Electronic Journal of Statistics, 13, 1135-1165.

• Du, L., Lan, W., Luo, R., & Zhong, P. (2018). Factor adjusted multiple testing of correlations. Computational Statistics & Data Analysis, 128, 34-47.

• Zhou, J., & Lan, W. (2018). Investor protection and cross-border acquisitions by Chinese listed firms: The moderating role of institutional shareholders. International Review of Economics and Finance, 56, 438-450.

• Lan, W., Feng, L., & Luo, R. (2018). Testing high dimensional linear asset pricing models. Journal of Financial Econometrics, 16, 191-210.

• Lan, W., Fang, Z., Wang, H., & Tsai, C. L. (2018). Covariance matrix estimation via network structure. Journal of Business & Economic Statistics, 36, 359-369.

• Lan, W., Ma, Y., Zhao, J., Wang, H., & Tsai, C. L. (2018). Sequential model averaging for high dimensional linear regression models. Statistica Sinica, 28, 449-469.

• Lan, W., Pan, R., Luo, R., & Chen, Y. (2017). High dimensional cross-sectional dependence test under arbitrary serial correlation. Science China-Mathematics, 60, 345-360.

• Zhong, P., Lan, W., Song, P., & Tsai, C. L. (2017). Tests for covariance structures with high dimensional repeated measurements. The Annals of Statistics, 45, 1185-1213.

• Luo, R., & Lan, W. (2017). Detecting homogeneous predictors in high dimensional panel model with a MCMC algorithm. Communication in Statistics-Simulation and Computation, 46, 7376-7392.

• Zou, T., Lan, W., Wang, H., & Tsai, C. L. (2017). Covariance regression analysis. Journal of the American Statistical Association, 112, 266-281.

• Zhou, J., Lan, W., & Tang, Y. (2016). The value of institutional shareholders: Evidence from cross-border acquisitions by Chinese listed firms. Management Decision, 54, 44-65.

• Zhou, J., Tam, O. K., & Lan, W. (2016). Solving agency problems in Chinese family firms-A law and finance perspective. Asian Business & Management, 15, 57-82.

• Lan, W., Zhong, P. S., Li, R., Wang, H., & Tsai, C. L. (2016). Testing a single regression coefficient in high dimensional linear models. Journal of Econometrics, 195, 154-168.

• Lan, W., Ding, Y., Fang, Z., & Fang, K. (2016). Testing covariates in high dimension linear regression with latent factors. Journal of Multivariate Analysis, 144, 25-37.

• Ma, Y., Lan, W., & Wang, H. (2015). A high dimensional two-sample test under a low dimensional factor structure. Journal of Multivariate Analysis, 140, 162-170.

• Ma, Y., Lan, W., & Wang, H. (2015). Testing predictor significance with ultra high dimensional multivariate responses. Computational Statistics & Data Analysis, 83, 275-286.

• Lan, W., Luo, R., Tsai, C. L., Wang, H., & Yang, Y. (2015). Testing the diagonality of a large covariance matrix in a regression setting. Journal of Business & Economic Statistics, 33, 77-86.

• Zhou, J., Tam, O. K., & Lan, W. (2015). Are investor protection and ownership concentration substitutes in Chinese family firms? Emerging Markets Finance and Trade, 51, 432-443.

• Lan, W., Wang, H., & Tsai, C. L. (2014). Testing covariates in high dimensional regression. Annals of the Institute of Statistical Mathematics, 66, 279-301.

• Lan, W., Wang, H., & Tsai, C. L. (2012). A Bayesian information criterion for portfolio selection. Computational Statistics & Data Analysis, 56, 88-99.

• 常琦, 雷博, 罗荣华, & 兰伟 (2024). 强制性社会责任报告披露政策的规制效用评估——基于同伴效应的视角. 统计研究, 41, 110-121.

• 丁月, 方匡南, 兰伟, & 徐顺 (2024). 基于网络关系的分类变量预测研究. 统计研究, 41, 148-156.

• 和泽慧, 路晓蒙, 罗荣华, & 兰伟 (2023). 打破刚性兑付,资金何去何从?——基于家庭资产配置的微观视角. 经济学季刊, 23, 1442-1460.

• 贺平, 兰伟, & 丁月 (2021). 中国股票市场可以预测吗？基于组合LASSO-logistic方法的视角. 统计研究, 38, 82-96.

• 严成樑, 李涛, & 兰伟 (2016). 金融开展、创新与二氧化碳排放. 金融研究, 1, 16-30.

• 罗荣华, 兰伟, & 杨云红 (2015). 基金排名与主动性水平：理论与实证. 中国管理科学, 23, 158-167.

• 罗荣华, 兰伟, & 杨云红 (2011). 基金的主动性管理提升了业绩吗. 金融研究, 10, 127-139.