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趙曉兵 (Zhao, Xiaobing)，博士 (PhD)，教授，博士生導師。

聯系地址： 杭州下沙學源街18號，浙江財經大學，新葡萄8883官網AMG

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

郵    編： 310018

最終學歷：

·2002.12-2006.12：香港理工大學，獲哲學博士學位（PhD)

博士后經歷：

·2006.10-2008.12: 華東師范大學金融與統計學院，博士后

研究方向：

• 生存分析（Survival Analysis)

• 保險精算 (Actuarial Studies)

• 復發事件分析 (Recurrent Events Data Analysis)

• 醫學統計 (Statistics in Medicine)

• 高維數據分析（Dimension Reduction Analysis)

  最近興趣：

• 海量數據分析（Massive data Analysis)

• 網絡數據分析（Networks data Analysis)

主持的國家級項目：

• 國家社會科學基金（一般項目），大數據環境下基于隨機塊模型的復雜網絡社區發現理論、算法和應用. 2018.07-2021.06, 項目編號：18BTJ023. 在研.

• 國家自然科學基金（面上項目），復發事件中高維協變量的降維技術及其應用研究, 2013.1-2016.12，項目編號: 11271317. 已經結題.

• 國家自然科學基金（面上項目），治愈模型和復發事件數據的聯合建模,推斷及應用, 2009.1-2011.12, 項目編號: 10871084, 已經結題.

  主持的省部級項目：

• 浙江省高校重大人文社科攻關計劃項目（規劃重點項目），海量數據下高維面板計數數據的穩健推斷及在社會醫療保險中的應用，項目編號：2018GH037，已經結題.

• 浙江省自然科學基金（一般項目），高維面板計數數據中基于分位數的降維及其應用研究， 2016.1-2018.12，項目編號: LY16A010007. 已經結題.

• 浙江省自然科學基金（一般項目），復發事件中高維協變量的充分降維技術及其應用， 2013.1-2014.12，項目編號: LY12A01017. 已經結題.

• 浙江省哲學社會科學規劃立項課題（一般項目），復發事件中高維協變量降維及在保險精算中應用， 2013.7-2015.7，項目編號: 12JCJJ18YB. 已經結題.

   

   

  學術兼職：

• 美國數學評論評論員（Mathematical Reviews)

• 國家自然科學基金項目同行評議專家

• 國家社科基金同行評議專家

• 浙江/黑龍江省自然科學基金評審專家

• 下列期刊匿名審稿人

  * 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，數學物理學報

  * Chinese Journal of Applied Probability and Statistics，應用概率統計

   

  主要海外經歷：

• 2009.03-2009.06：澳大利亞麥考瑞大學精算系（Macquarie University)

• 2014.09-2015.08：美國西北大學醫學院預防醫學系（Northwestern University)

   

  入選人才情況：

• 浙江省高校中青年科學帶頭人

• 浙江財經大學杰出中青年教師資助計劃（A類）

   

  主要學術論文：

  1. 已經發表或接收：

  [1]Feng, Y., Zhao, X.B. (趙曉兵) 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. (趙曉兵). (2020). Effi-

  cient estimation of panel count data with dependent observation process.

  Preprint: Journal of Statistical Computation and Simulation

  [3]Zhao, X.B. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵), 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. (趙曉兵). (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. (趙曉兵), 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵)  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. (趙曉兵)  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. (趙曉兵), 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵), 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵). (2009). The credibility premiums under generalized weighted loss functions. Journal of Industrial and Management Optimization, 5(4), 893-910.

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

  [31]Zhao, X.B. (趙曉兵), 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. (趙曉兵) 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. (趙曉兵) 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. (趙曉兵) and Zhou, X. (2006). Proportional hazards models for survival data with long-term survivors. Statistics and Probability Letters, 76, 1685-1893.