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教授
鲍建海 陈永川
段玉萍 甘在会
高维东 季 青
蒋仁进 李康伟
邵井海 孙笑涛
王凤雨 汪更生
吴偶 吴奕飞
张勇 宗传明
兼职教授
陈化 Peter Paule
副教授
陈鑫 戴 嵩
范协铨 何玲
胡二彦 黎怀谦
沈瑞鹏 宋保方
田文义 王耀宏
吴华明 吴杰
徐 甜 余讯
张海祥 周明铄
朱斐
讲师
邓英俊 郭嘉祥
黄昊阳 黄兴
梁聪 马文钧
宋基建 魏斌
谢满庭 杨松
张与彪 赵铭锋
博士后
申佳 2020-2023
唐大钊 2019-2021
办公室
刘阳 李浡然
吕秀杰 钟小平
张海祥 副教授    
应用数学中心教师 主 页
  电 话:
  邮 箱: haixiang.zhang@tju.edu.cn

研究方向:

大数据统计推断;中介分析;微生物组数据分析

教育经历:

2003.09-2007.07  吉林大学 统计学  本科/学士

2007.09-2009.07  吉林大学 概率论与数理统计  研究生/硕士

2009.09-2012.12  吉林大学 概率论与数理统计  研究生/博士

2011.09-2012.09  美国密苏里大学 统计学系  国家公派联合培养博士研究生

工作经历:

2013.05-2015.05  中国科学院应用数学研究所/博士后

2015.05-2016.05  美国西北大学/博士后

2013.04-2016.07  吉林大学数学学院/讲师

2016.08-至今      天津应用数学中心/副教授

代表性论文与著作:(#表示通讯作者)

[1] Zhang, H. and Wang, H. (2020). Distributed subdata Selection for big data via sampling-based approach. Computational Statistics and Data Analysis. In press.

[2] Zuo, L., Zhang, H.#, Wang, H. and Liu, L. (2020). Sampling-based estimation for massive survival data with additive hazards model. Statistics in Medicine. In press. (The first author is a Master student under my supervision).

[3] Zhang, H., Huang, J. and Sun, L. (2020). A rank-based approach to estimating monotone individualized two treatment regimes. Computational Statistics and Data Analysis. In press.

[4] Zhang, H., Chen, J., Feng, Y., Wang, C., Li, H. and Liu, L. (2020). Mediation effect Selection in high-dimensional and compositional microbiome data. Statistics in Medicine. In press.

[5] Liu, J. and Zhang, H.# (2020). First-order random coefficient INAR process with dependent counting series. Communications in Statistics: Simulation and Computation. In press. (The first author is a Master student under my supervision).

[6] Zhang, H., Chen, J., Li, Z. and Liu, L. (2020). Testing for mediation effect with application to human microbiome data. Statistics in Biosciences. In press.

[7] Wang, Y. and Zhang, H.# (2020). Some estimation and forecasting procedures in Possion-Lindley INAR(1) process. Communications in Statistics: Simulation and Computation. In press. (The first author is a Master student under my supervision).

[8] Wang, X., Wang, D. and Zhang, H. (2020). Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure. Statistical Papers, 61, 245-260.

[9] Zhang, H.#, Wang, D. and Sun, L. (2017). Regularized estimation in GINAR(p) process. Journal of the Korean Statistical Society, 46, 502-517.

[10] 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.

[11] 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, 23, 560-584.

[12] 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 high-dimensional multivariate regression, with application to a DNA methylation study. Statistical Applications in Genetics and Molecular Biology, 16, 159-171.

[13] .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.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

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

[19] 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.

[20] 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.

[21] 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.

[22] Zhang, H., Sun, J. and Wang, D. (2013). Variable Selection and estimation for multivariate panel count data via the seamless Lo penalty. The Canadian Journal of Statistics, 41, 368-385.

[23] 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.

[24] 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.

[25] 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.

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

[27] 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.