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张海祥

张海祥

职称:
副教授
院系:
应用数学中心
电子邮箱:
haixiang.zhang@tju.edu.cn
办公地点:
卫津路校区25教

研究方向

高维数据;生物统计;时间序列分析

教育背景

2003.09 - 2007.07   吉林大学     学士
2007.09 - 2009.07   吉林大学     硕士
2009.09 - 2012.12   吉林大学     博士
2011.09 - 2012.09   美国密苏里大学    国家公派联合培养博士生

工作经历

2013.04 - 2016.11  吉林大学数学学院    讲师
2013.05 - 2015.05  中国科学院数学与系统科学研究院   博士后
2015.05 - 2016.05  美国西北大学   博士后
2016.12 -  今           天津大学应用数学中心   副教授

教学工作

学生指导 指导硕士生 2人 (在读),博士生0人
竞赛指导 数学竞赛、数学建模获奖情况
教改项目 (只填写省部级以上的) 身份

科研工作

基金项目   身份
2014.05-2015.05    中国博士后科学基金(面板计数数据的统计推断)                主持人
2014.01-2016.12    国家自然科学基金(多元线性整值时间序列的统计分析)      主持人

主要荣誉

吉林省自然科学学术成果奖 (2012);
第十一届全国统计科研优秀成果奖(2013);
教育部自然科学二等奖(2016).
 

学术兼职

其它

发表论文

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    1.Zhang, H., Sun, L., Zhou, Y. and Huang, J. (2017). Oracle inequalities and selection consistency for weighted lasso in high-dimensional additive hazards model. Statistica Sinica, 27, 1903-1920.

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    2.Zhang, H., Wang, D. and Sun, L. (2017). Regularized estimation in GINAR(p) process. Journal of the Korean Statistical Society, In press.

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    3.Zhou, J., Zhang, H., Sun, L. and Sun, J. (2017). Joint analysis of panel count data with informative observation process and a dependent terminal event. Lifetime Data Analysis, In press.

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    4.Fang, S., Zhang, H., Sun, L. and Wang, D. (2017). Analysis of panel count data with time-dependent covariates and informative observation process. Acta Mathematicae Applicatae Sinica(English Series), 33, 147-156.

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    5.Yoon, G., Zheng, Y., Zhang, Z., Zhang, H., Gao, T., Joyce, B., Zhang, W., Guan, W., Baccarelli, A., Jiang, W., Schwartz, J., Vokonas, P., Hou, L. and Liu, L.(2017). Ultra-high dimensional variable selection with application to normative aging study: DNA methylation and metabolic syndrome. BMC Bioinformatics, 18:156.

  • 6.Zhang, H., Zheng, Y., Yoon, G., Zhang, Z., Gao, T., Joyce, B., Zhang, W., Schwartz, J., Vokonas, P., Colicino, E., Baccarelli, A., Hou, L. and Liu, L. (2017). Regularized estimation in sparse highdimensional multivariate regression, with application to a DNA methylation study. Statistical Applications in Genetics and Molecular Biology, 16, 159 - 171.

  • 7.Wang, X, Wang, D. and Zhang, H. (2017). Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure. Statistical Papers. In press.

  • 8.Zhang, H., Zheng, Y., Zhang, Z., Gao, T., Joyce, B., Yoon, G., Zhang, W., Schwartz, J., Just, A., Colicino, E., Vokonas, P., Zhao, L., Lv, J., Baccarelli, A.,Hou, L. and Liu, L. (2016). Estimating and testing high-dimensional mediation effects in epigenetic studies. Bioinformatics, 32, 3150–3154

  • 9.Fang, S., Zhang, H. and Sun, L. (2016). Joint analysis of longitudinal data with additive mixed effect model for informative observation times. Journal of Statistical Planning and Inference,169, 43-55.

  • 10.Liu, Y., Wang, D., Zhang, H. and Shi, N. (2016). Bivariate zero truncated Poisson INAR(1) process. Journal of the Korean Statistical Society.45, 260-275

  • 11.Li, C., Wang, D. and Zhang, H. (2015). First-order mixed integer-valued autoregressive processes with zero-inflated generalized power series innovations. Journal of the Korean Statistical Society, 44, 232-246.

  • 12.Zhang, H. and Wang, D. (2015). Inference for random coefficient INAR(1) process based on frequency domain analysis, Communications in Statistics: Simulation and Computation, 44, 1078-1100.

  • 13.Jia, B., Wang, D. and Zhang, H.(2014). A study for missing values in PINAR(1) processes. Communications in Statistics: Theory and Methods, 43, 4780-4789.

  • 14.Zhang, H., Wang, D. and Zhu, F. (2012). Generalized RCINAR(1) process with signed thinning operator. Communications in Statistics: Theory and Methods,41, 1750-1770.

  • 15.Zhang, H., Zhao, H., Sun, J., Wang, D. and Kim, K. (2013). Regression analysis of multivariate panel count data with an informative observation process. Journal of Multivariate Analysis, 119, 71-80.

  • 16.Zhang, H., Sun, J. and Wang, D. (2013). Variable selection and estimation for multivariate panel count data via the seamless-L0 penalty. The Canadian Journal of Statistics, 41, 368-385.

  • 17.Zhang, H., Wang, D. and Zhu, F. (2011). Empirical likelihood inference for random coefficient INAR(p) process. Journal of Time Series Analysis, 32, 195-203.

  • 18.Zhang, H., Wang, D. and Zhu, F. (2011). The empirical likelihood for first-order random coefficient integer-valued autoregressive processes. Communications in Statistics: Theory and Methods, 40, 492-509.

  • 19.Wang, D. and Zhang, H. (2011). Generalized RCINAR(p) process with signed thinning operator. Communications in Statistics: Simulation and Computation, 40, 13-44.

  • 20.Zhang, H., Wang, D. and Zhu, F. (2010). Inference for INAR(p) processes with signed generalized power serie thinning operator. Journal of Statistical Planning and Inference, 140, 667-683.

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