赵晓兵博士，现为浙江财经大学数据科学学院教授, 博士生导师。浙江省高校中青年学科带头人、浙江财经大学杰出中青年教师资助计划人选(A类)。赵晓兵2006年12月博士(PhD)毕业于香港理工大学应用数学系（统计方向，导师：Xian Zhou），2008年12月华东师范大学统计系博士后出站(合作教授：王静龙），是美国数学评论评论员、国家自然科学基金通讯评审专家、国家社科基金评审/结题鉴定专家、浙江，广东、黑龙江等多个省份的自然科学基金通讯评审专家，也是包括Journal of the American Statistical Association，Biometrics, Scandinavian Journal of Statistics，Statistics in Medicine等国际国内主流期刊的匿名评阅人。赵晓兵博士一直从事生存分析和保险精算等的研究，主要包括治愈模型、复发事件模型、面板计数数据、非参数模型、半参数模型、高维数据降维、海量数据分析、大规模网络数据分析等方面的研究，在国际主流统计学和精算学期刊,例如 Statistics in Medicine; Statistica Sinica; Journal of Multivariate Analysis, Journal of Statistical Planning and Inference ; Computational Statistics and Data Analysis; Lifetime Data Analysis; Insurance: Mathematics and Economics等发表论文60多篇。
最终学历
[1] 2002年12月~2006年12月， 香港理工大学 ，统计学 ，博士研究生
博士后经历
[1] 2006.10-2008.12: 华东师范大学金融与统计学院，博士后
工作经历
[1] 2001年01月~2010年04月， 江南大学
[1] 2002年04月~至今， 浙江财经大学
海外经历（部分）
[1] 2009.03-2009.06：澳大利亚麦考瑞大学精算系（Macquarie University)
[2] 2014.09-2015.08：美国西北大学医学院预防医学系（Northwestern University)
人才情况
[1] 浙江省高校中青年科学带头人
[2] 浙江财经大学杰出中青年教师资助计划（A类）
社会兼职
[1] 美国数学评论评论员（Mathematical Reviews)
[2] 国家自然科学基金项目同行评议专家
[3] 国家社科基金同行评议/结题鉴定专家
[4] 浙江省、广东省、黑龙江省等自然科学基金评审专家
[5] 下列期刊匿名审稿人
* Journal of the American Statistical Association
* Biometrics
* Scandinavian Journal of Statistics
* Statistics in Medicine
* Journal of Applied Statistics
* Journal of Statistical Computation and Simulation
* Journal of Nonparametric Statistics
* Journal of Biopharmaceutical Statistics
* Communication in Statistics: Simulation and Computation
* Communication in Statistics: Theory and Methods
* Journal of Systems Science and Complexity
* Computer Methods and Programs in Biomedicine
* Acta Mathematica Scientia，数学物理学报
* Chinese Journal of Applied Probability and Statistics，应用概率统计
研究方向
生存分析（Survival Analysis)
保险精算 (Actuarial Studies)
复发事件分析 (Recurrent Events Data Analysis)
医学统计 (Statistics in Medicine)
高维数据分析（Dimension Reduction Analysis)
最近兴趣
海量数据分析（Massive Data Analysis)
因果推断（Causal Inference）
网络数据分析（Networks Data Analysis)
科研项目
[1] 国家社会科学基金（一般项目），大规模面板计数数据的高维协变量的稳健降维和应用研究. 2022.07-2025.06, 项目编号：22BTJ069. 已经结题.
[2] 国家社会科学基金（一般项目），大数据环境下基于随机块模型的复杂网络社区发现理论、算法和应用. 2018.07-2021.06, 项目编号：18BTJ023. 已经结题.
[3] 国家自然科学基金（面上项目），复发事件中高维协变量的降维技术及其应用研究, 2013.1-2016.12，项目编号: 11271317. 已经结题.
[4] 国家自然科学基金（面上项目），治愈模型和复发事件数据的联合建模,推断及应用, 2009.1-2011.12, 项目编号: 10871084, 已经结题.
[5] 浙江省自然科学基金（一般项目），大规模动态网络数据的统计建模理论、算法和应用, 2025.1-2026.12，项目编号: LY12A01017. 在研.
[6] 2024年度浙江省统计重大研究课题，大规模动态网络的统计分析与算法研究，项目编号：24TJZD03. 在研.
[7] 浙江省高校重大人文社科攻关计划项目（规划重点项目），海量数据下高维面板计数数据的稳健推断及在社会医疗保险中的应用，项目编号：2018GH037，已经结题.
[8] 浙江省自然科学基金（一般项目），高维面板计数数据中基于分位数的降维及其应用研究， 2016.1-2018.12，项目编号: LY16A010007. 已经结题.
[9] 浙江省自然科学基金（一般项目），复发事件中高维协变量的充分降维技术及其应用， 2013.1-2014.12，项目编号: LY12A01017. 已经结题.
[10] 浙江省哲学社会科学规划立项课题（一般项目），复发事件中高维协变量降维及在保险精算中应用， 2013.7-2015.7，项目编号: 12JCJJ18YB. 已经结题.
论文成果（部分）
[1] Li,Yangkuo，Xia,Yufeng and Zhao, X.B. (赵晓兵) . (2026). Statistical inference for dynamic stochastic block model in temporal networks, Journal of Nonparametric Statistics, https://doi.org/10.1080/10485252.2026.2675341.
[2]Wang, W.W., Wang,Y.J. and Zhao, X.B. (赵晓兵). (2026). Estimation and inference for fixed center effects on panel count data, Statistical Papers，67(27), https://doi.org/ 10. 1007/ s00362-026-01807-0.
[3]Xia,Y.F., Li, Y.K. Zhao, X.B. (赵晓兵), and Xu, X. (2025). Investigating network structures in recurrent event data with discrete observation times, Lifetime Data Analysis, 31, 543-573.
[5]Wang, W.W., Zhiyang,Cui, Ruijie,Cheng,Wang,Y.J. and Zhao, X.B. (赵晓兵). (2024). Regression analysis of clustered panel count data with additive mean models, Statistical Papers，65(5), 2915-2936.
[6]Wang, W.W., Wang,Y.J. and Zhao, X.B. (赵晓兵). (2023). Polynomial spline estimation of panel count data model with an unknown link function, Statistical Papers，64(6), 1805-1832.
[7]Wang, W.W., Wang,Y.J. and Zhao, X.B. (赵晓兵). (2022). Semiparametric analysis of multivariate panel count data with nonlinear interactions.. Lifetime Data Analysis, 22,89-115.
[8]Wang, W.W., Wang,Y.J. and Zhao, X.B. (赵晓兵). (2022). Local logarithm partial likelihood estimation of panel count data model with an unknown link function. Computational Statistics & Data Analysis, 116, 107346.
[9]Zheng,Y.Q., Zhao, X.B. (赵晓兵) and Zhang, X. Q. (2022). Quantile regression for massive data with network-induced dependence, and application to the New York statewide planning and research cooperative system. Communications in Statistics-Simulation and Computation，51（9）,2962-2993.
[10]Wang, W.W., Wang,Y.J., Wu, X.Y. and Zhao, X.B. (赵晓兵). (2021). Efficient estimation
of panel count data with dependent observation process. Journal of Statistical Computation and Simulation，19,464-476.
[11]Feng, Y., Zhao, X.B. (赵晓兵) and Zhou, X. (2020). Semiparametric random censorship models for survival data with long-term survivors. Communications in Statistics-Simulation and Computation, 49 (11), 2876-2896.
[12]Zhao, X.B. (赵晓兵) and Zhou, X. (2020). Partial sufficient dimension reduction on additive rates model for recurrent event data with high-dimensional covariates. Statistical Papers, 61,523-541.
[13]Wang, W. W., Wu, X. Y. , Zhao, X.B. (赵晓兵) and Zhou. X. (2020). Quantile Regression of Panel Count Data on Quadratic Inference Functions. Journal of Statistical Planning and Inference, 207,230-245.
[14]Zhang, X. Q., Zhao, X.B. (赵晓兵) and Zheng, Y.Q. (2020). A novel approach to estimate the Cox model with temporal covariates and application to medical cost data. Communications in Statistics-Simulation and Computation，49(18), 4520-4539.
[15]Wang, W. W., Wu, X. Y. , Zhao, X.B. (赵晓兵) and Zhou. X. (2019). Quantile Estimation of Partially Varying Coefficient Model for Panel Count Data with Informative Observation Times. Journal of Nonparametric Statistics, 31(4), 932-951.
[16]Zheng, Y.Q., Zhao, X.B. (赵晓兵), Zhang, X. Q.，Ye, X.Y. and Dai, Q. W. (2019). Mining the hidden link structure from distribution flows for a spatial social network. Preprint: Complexity.
[17]Wang, W. W., Wu, X. Y. Zhang, X.Q. and Zhao, X.B. (赵晓兵). (2019). Partial sufficient dimension reduction on the joint model of recurrent events and terminal events. Journal of Applied Statistics, 46, 522-541.
[18]Zhao, X.B. (赵晓兵), Wang, W. W., Liu, L. and Shih, T. (2018). A flexible quantile regression model for medical costs, with application to medical expenditure panel survey study costs. Statistics in Medicine, 37, 2645-2666.
[19]Wang, W. W., Wu, X. Y. , Zhao, X.B. (赵晓兵) and Zhou. X. (2018). Robust variable selection of joint frailty model for panel count data. Journal of Multivariate Analysis, 167，60-78.
[20]Zheng, Y.Q., Zhao, X.B. (赵晓兵) and Zhang, X. Q. (2018). Understand dynamic status-change of hospital stay and cost accumulation by a differential equation- based combination of continuous and finitely-jumped processes. Computational and Mathematical Methods in Medicine.
[21]Zhao, X.B. (赵晓兵) and Zhou, X. (2017). Multi-type insurance claim processes with high-dimensional covariates. Communications in Statistics-Simulation and Computation, 46，500-514.
[22]Zhao, X.B. (赵晓兵) and Zhou. X. (2015). Semiparametric models of longitudinal and time-to-event data with applications to HIV viral dynamics and CD4 counts. Journal of Applied Statistics, 42, 2461-2477.
[23]Zhao, X.B. (赵晓兵) and Zhou. X.(2015). Estimation of copula based models for lifetime medical costs. Annals of the Institute of Statistical Mathematics, 67, 897-915.
[24]Zhao, X.B. (赵晓兵), Wang, J. L., Zhou, X. and Zhu, Z. Y. (2015). Recurrent events analysis in the presence of terminal event and zero-recurrence subjects. Communications in Statistics -Theory and Methods, 44, 710-725.
[25]Zhao, X.B. (赵晓兵) and Zhou, X. (2014). Copula-based dependency between frequency and class in car insurance with excess-zeros. Operations Research Letters, 42, 273-277.
[26]Zhao, X.B. (赵晓兵) and Zhou, X. (2014). Sufficient dimension reduction on the mean and rate functions of recurrent events. Statistics in Medicine, 33, 3693-3709
[27]Zhao, X.B. (赵晓兵) and Zhou, X. (2014). Sufficient dimension reduction on marginal regression for gaps of recurrent events. Journal of Multivariate Analysis，127，56-71.
[28]Zhao, X.B. (赵晓兵) and Zhou, X. (2012). Estimation of medical costs by copula models with dynamic change of health status. Insurance: Mathematics and Economics, 51, 480-491.
[29]Zhao, X.B. (赵晓兵), Zhou, X. and Wang, J. L. (2012). Semiparmetric model for recurrent events data with cure fraction and informative censoring. Journal of Statistical Planning and Inference, 141, 289-300.
[30]Zhao, X.B. (赵晓兵) and Zhou, X. (2012). Modeling gap times between recurrent events by marginal rate function. Computational Statistics and Data Analysis, 56, 370-383.
[31]Zhao, X.B. (赵晓兵) and Zhou, X. (2012). Estimation of copula-based insurance claim numbers with excess zeros. Insurance: Mathematics and Economics, 50,191-199.
[32]Zhao, X.B. (赵晓兵) and Zhou, X. (2012). Measurement error in proportional hazards models for survival data with long-term survivors. Acta Mathematicae Applicatae Sinica, English Series, 28(2)，275-288.
[33]Zhao, X.B. (赵晓兵) and Zhou, X. (2010). Empirical receiver operating characteristic curve for two-sample comparison with cure fractions, Lifetime Data Analysis, 16，316-332.
[34]Zhao, X.B. (赵晓兵) and Zhou, X. (2010). Semiparametric estimation in transformation models with cure fraction, Communications in Statistics-Theory and Methods, 39，3371-3388.
[35]Zhao, X.B. (赵晓兵) and Zhou, X. (2010). Applying copula models to individual claim loss reserving methods. Insurance: Mathematics and Economics, 46, 290-299.
[36]Wen, L.M., Wu, X.Y. and Zhao, X.B. (赵晓兵). (2009). The credibility premiums under generalized weighted loss functions. Journal of Industrial and Management Optimization, 5(4), 893-910.
[37]Zhao, X.B. (赵晓兵), Wang, J. L. and Zhou, X. (2009). Semiparametric model for prediction of individual claim loss reserving. Insurance: Mathematics and Economics, 45, 1-8.
[38]Zhao, X.B. (赵晓兵) and Zhou, X. (2009). Semiparametric modeling of cost data containing zeros. Statistics and Probability Letters, 79,1207-1214.
[39]Zhao, X.B. (赵晓兵), Wu X.Y. and Zhou, X. (2009). A Change-point model for survival data with long-term survivors. Statistica Sinica, 377-390
[40]Zhao, X.B. (赵晓兵) and Zhou, X. (2008). Discrete-time survival analysis for survival data with long-term survivors. Statistics in Medicine, 27, 1261-1281.
[41]Zhao, X.B. (赵晓兵) and Zhou, X and Wu, X.Y. (2007). Local linear regression in proportional hazards model with censored data. Communications in Statistics -Theory and Methods, 36, 2761-2776.
[42]Zhao, X.B. (赵晓兵) and Zhou, X. (2006). Proportional hazards models for survival data with long-term survivors. Statistics and Probability Letters, 76, 1685-1893.
中文期刊论文（部分）
[1] 赵晓兵，陈思言，孙满贞. (2025). 深度学习赋能的新质生产力水平测度. 浙江大学学报（理学版），https://link.cnki.net/urlid/33.1246.N.20251224.1415.004.
[2] 徐璇，傅梦怡，赵晓兵. (2025). 海量面板计数数据的分位数回归模型及在社交网络分析中的应用。 数理统计与管理，44(3), 407-426。
[3] 赵晓兵，李洋阔，徐菁. (2024). 网络观测数据下考虑邻域处理的因果推断及在政策评价上的应用研究。统计与信息论坛，39(11), 3-17.
[4] 赵晓兵，王佳顺. (2023). 基于复发瞬间链接间隔的动态网络社区发现. 统计与信息论坛， 38(4): 3-18.
[5] 潘青，赵晓兵. (2022). 多指标可加模型及在医疗费用预测中的应用. 应用概率统计， 38(1),42-53.
[6] 杨晓，赵晓兵. (2021). 基于协变量的混合隶属度随机块模型的社区发现方法. 统计与决策，584(20), 15-19.
[7] 赵晓兵，刘伟. (2015). 均值计数模型下汽车保险索赔频数的估计方法. 财经论丛，191(2), 44-49. 人大复印报刊资料全文收录.
[8] 徐国盛，赵晓兵. (2014). 变量选择方法在医疗保险赔付评估中的应用. 统计与信息论坛. 29(11), 59-64.
[9] 冯彦，徐璇，赵晓兵. (2015). 部分非线性指标模型的降维及在医疗费用评估中的应用. 中国卫生统计，34(6), 995-998.
[10] 赵晓兵，王伟伟. (2013). 高维附加信息下的商业医疗保险费用评估模型和方法.财论论丛，173(4), 58-65. 人大复印报刊资料全文收录.
[11] 杨冰，赵晓兵. (2012). Cox模型及其相关问题的延伸. 江南大学学报 (自然科学版)，9(50), 619-624.
[12] 肖翠柳，赵晓兵，王静龙. (2010). Nonparametric estimation of a recurrent survival function with long-term survivors. 应用概率统计，26(4), 159-170.