赵晓兵 (Zhao, Xiaobing)，博士 (PhD)，教授，博士生导师。

联系地址： 杭州下沙学源街18号，浙江财经大学，77779193永利官网

电子信箱： maxbzhao@126.com/maxbzhao@zufe.edu.cn

邮    编： 310018

最终学历：

·2002.12-2006.12：香港理工大学，获哲学博士学位（PhD)

博士后经历：

·2006.10-2008.12: 华东师范大学金融与统计学院，博士后

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)

• 网络数据分析（Networks data Analysis)

主持的国家级项目：

• 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. 在研.

• 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.

• 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.

  主持的省部级项目：

• 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.

• 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.

• 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.

• 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.

   

   

  学术兼职：

• 美国数学评论评论员（Mathematical Reviews)

• 国家自然科学基金项目同行评议专家

• 国家社科基金同行评议专家

• 浙江/黑龙江省自然科学基金评审专家

• 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

  * 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

   

  主要海外经历：

• 2009.03-2009.06：澳大利亚麦考瑞大学精算系（Macquarie University)

• 2014.09-2015.08：美国西北大学医学院预防医学系（Northwestern University)

   

  入选人才情况：

• 浙江省高校中青年科学带头人

• 浙江财经大学杰出中青年教师资助计划（A类）

   

  主要学术论文：

  1. 已经发表或接收：

  [1]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, 2876-2896.

  [2]Wang, W.W., Wang,Y.J., Wu, X.Y. and Zhao, X.B. (赵晓兵). (2020). Effi-

  cient estimation of panel count data with dependent observation process.

  Preprint: Journal of Statistical Computation and Simulation

  [3]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.

  [4]Zheng,Y.Q., Zhao, X.B. (赵晓兵) and Zhang, X. Q. (2020). Quantile regres -sion for massive data with network-induced dependence, and application to the New York statewide planning and research cooperative system. Preprint: Communications in Statistics-Simulation and Computation.

  [5]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.

  [6]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，18,4520-4539.

  [7]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.

  [8]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.

  [9]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.

  [10]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.

  [11]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.

  [12]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.

  [13]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.

  [14]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.

  [15]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.

  [16]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.

  [17]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.

  [18]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

  [19]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.

  [20]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.

  [21]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.

  [22]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.

  [23]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.

  [24]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.

  [25]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.

  [26]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.

  [27]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.

  [28]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.

  [29]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.

  [30]Zhao, X.B. (赵晓兵) and Zhou, X. (2009). Semiparametric modeling of cost data containing zeros. Statistics and Probability Letters, 79,1207-1214.

  [31]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

  [32]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.

  [33]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.

  [34]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.