张三国-中国科学院大学-UCAS
[中文]
[English]
招生信息
教育背景
出版信息
科研活动
工作经历
基本信息
张三国 男 教授、博导 电子邮件：sgzhang # ucas.ac.cn 联系电话： 010-88256077通信地址： 北京市石景山区玉泉路19号（甲）中国科学院大学数学科学学院 邮政编码： 100049
研究领域
高维数据分析；参数与非参数统计；测量误差校正模型；变点分析；机器学习
招生信息
招生方向
非参数统计,网络数据统计分析
教育背景
教育背景
1998.7~2002.6 理学博士学位 中国科学技术大学 1994.7~1998.6 理学学士学位 中国科学技术大学 2003.7~2004.8 香港中文大学统计学系 博士后 2007.1~2008.8 美国Vanderbilt大学医学中心医学与公众健康研究所和生物统计系
出版信息
部分发表论文：‍‍‍‍1) Zhang, S. & Chen, X. Consistency of modified MLE in EV model with replicated observations. Science in China (Series A), 304-310,2001.2) Zhang, S. & Chen, X. Estimation in the polynomial errors-in-variables model. Science in China (Series A), 1-8, 2002.3) Zhang, S. & Chen, X. Asymptotic normality of parameters estimation in EV model with replicated observations. Acta Mathematica Scientia (Series B), 107-114, 2002.4) Zhang, S. & Chen, X. On asymptotic normality of parameters in linear EV model. Chinese Annals of Mathematics (Series B), 495-506, 2002.5) Liu, J. Zhang, S. & Chen, X. Linear EV model with replicable observed independent variables, Science in China Series A: Mathematics, 752-769, 2006.6) Zhang, S. & Liao, Y. On some problems of weak consistency of quasi-maximum likelihood estimates in generalized linear models, Science in China Series A: Mathematics, 1287-1296, 2008.7) Jia, Y., Sun, J., Fan, L., Song, D., Tian, S., Yang, Y., Jia, M., Lu, L., Sun, X. Zhang, S., Kulczycki, A. & Vermund, H. S. Estimates of HIV prevalence in a highly endemic area of China: Dehong Prefecture,Yunnan Province, International Journal of Epidemiology, 1287-1296, 2008.8) Yu, C., Zhang, S., Zhou, C. & Sile, S. A likelihood test of population Hardy Weinberg Equilibrium for case-control studies. Genetic Epidemiology, 275－280, 2009.9) Ning, W., Zhang, S. & Yu, C. A Moment-based Test for the Homogeneity in Natural Exponential Family with Quadratic Variance Functions, Statistics and Probability Letters, 828－834, 2009.10) Ning, W., Gupta, A., K., Yu, C. & Zhang, S., A moment-based test for homogeneity in finite mixture models, Communications in Statistics - Theory and Methods, 1371-1382, 2009. 11) Zhang, B., Halder, K. S., Zhang, S. & Datta, K. P. Targeting transforming growth factor-beta signaling in liver metastasis of colon cancer. Cancer letters, 114-120, 2009.12) Wang, G., Zhang, S., Joggerst, S. J., McPherson, J. & Zhao, X. D. Effects of the number and interval of balloon inflations during primary PCI on the extent of myocardial injury in patients with STEMI: Does postconditioning exist in real-world practice? Journal of Invasive Cardiology, 451-455, 2009.13) Huang, F., Jiang, Z., Zhang, S. & Gao, S. Reliability evaluation of wireless sensor networks using logistic regression, International Conference on Communications and Mobile Computing, IEEE Computer Society, 334-338, 2010.14) Wang, S., Zhang, S. & Xue, H. Sieve least squares estimator for partial linear models with current status data, Journal of Systems Science and Complexity, 335-346, 2011.15) Jiang, J., Zhang, S., Guo, T. Russo’s formula, uniqueness of the infinite cluster, and continuous differentiability of free energy for continuum percolation, Journal of Applied Probability, 597-610, 2011. 16) Zhang, S., Liao, Y. & Ning, W. Asymptotic properties of quasi-Maximum likelihood estimates in generalized linear models, Communications in Statistics - Theory and Methods, 4417-4430, 2011.17) Shi, Y. Li, T. Wang, Y. Gao, Q. Zhang, S. & Li, H. Optical image encryption via ptychography, Optics Letters, 1425-1427, 2013.18) Liu，J. Chang，N. Zhang，S. & Lei，Z. Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis，International Journal of Communication Systems，28:1884–1897, 2015.19) Wu，X. Zhang，Q. & Zhang, S. Detecting difference between coeficients in linear model using jackknife empirical likelihood，Journal of Systems Science and Complexity，29:542-556, 2016.20) Zhang, Q., Zhang, S. Liu, J. Huang, J. & Ma, S. Panelized integrative analysis under the accelerated failure time model, Statistica Sinica, 26:493-508, 2016.21) Wu，X. Zhang，S. Zhang, Q. & Ma，S. Detecting change point in linear regression using jackknife empirical likelihood，Statistics and its interface，9: 113–122, 2016.22)Zang, Y. Zhang, S. Li, Q. Zhang, Q. Jackknife empirical likelihood test for high-dimensional regression coefficients, Computational Statistics & Data Analysis, 94:302–316, 2016.23) Hu, X., Zhang W, Zhang S, Ma S, & Li. Q. Group-combined p-values with applications to genetic association studies. Bioinformatics, 32, 2737–2743, 2016.24) Zang, Y., Zhao, Y., Zhang, Q., Cai, H., Zhang, S. & Ma, S. Identifying Gene-Environment Interactions with a Least Relative Error Approach, Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics: Selected Papers from the 2015 ICSA/Graybill Applied Statistics Symposium, Colorado State University, Fort Collins[M]. Springer, 305-321, 2016.25) Zang, Y. Zhang, Q., Zhang, S. Li, Q. & Ma, S. Empirical likelihood test for high dimensional generalized linear models. Invited book chapter. Big and Complex Data Analysis: Statistical Methodologies and Applications, Springer, 29-50, 2017. 26) Wang, G., Zhang, Q., Zang, Y., Zhang, S. & Ma, S. Identifying gene-environment interactions associated with prognosis using penalized robust regression. Invited book chapter. Big and Complex Data Analysis: Statistical Methodologies and Applications, Springer. 347-367, 2017. 27) Hu, X., Duan, X., Pan, D., Zhang, S., & Li, Q. A Model-embedded Trend Test with Incorporating Hardy-Weinberg Equilibrium Information. Journal of Systems Science and Complexity, 101-110, 2017.28) Huang, Y., Zhang, Q., Zhang, S., Huang, J. & Ma, S. Promoting similarity of sparsity structures in intergrative analysis with penalization. Journal of American Statistical Association, 342-350, 2017.29) Wu，X. Zhang，S. & Zhang，Q. A note on the two sample mean problem based on jackknife empirical likelihood，Communications in Statistics - Theory and Methods，7827-7836, 2017. 30) Wang, G., Zhang，S., & Dai, P. A Robust image denoising algorithm based on Exponential squared loss and SELO penalty,Acta Mathematicae Applicatae Sinica, English Series，753-770, 2017. 31) Zang, Y., Zhao, Q., Zhang, Q., Li, Y., Zhang，S. & Ma, S. Inferring gene regulatory relations using high-dimensional robust estimation. Genetic Epidemiology, 437-454, 2017. 32) Wu, M.,Zang, Y., Zhang，S., Huang, J. & Ma, S. Accommodating missingness in environmental measurements in gene-environment interaction analysis. Genetic Epidemiology, 523-554, 2017. 33) Fu, S., Zhang, S. & Liu, Y. Adaptively weighted large-margin angle-based classifiers, Journal of multivariate analysis, 282-299, 2018.34) Li, J., Zhang, W., Zhang, S. & Li, Q. A theoretic study of a distance-based regression model, Science China Mathematics, 979-998, 2019.35) Fu, S., He, Q., Zhang, S. & Liu, Y. Robust outcome weighted learning for optimal individualized treatment rules, Journal of biopharmaceutical statistics, 606-624, 2019.36) Xue, Y., Wang, J., Ding, J., Zhang, S. & Li, Q. A powerful test for ordinal trait genetic association analysis, Statistical Applications in Genetics and Molecular Biology, vol. 18, issue 2, 2019.37) Xue, Y., Ding, J., Wang, J.，Zhang, S. & Pan, D. Two-phase SSU and SKAT in genetic association studies. Journal of Genetics, 99:9, 2020.38) Zhang, S., Xue, Y., Zhang, Q., Ma, C., Wu, M. & Ma, S. Identification of gene–environment interactions with marginal penalization. Genetic Epidemiology, 44:159–196, 2020.39) Bu, D., Yang, Q., Meng, Z., Zhang, S. & Li, Q. Truncated tests for combining evidence of summary statistics. Genetic Epidemiology. 44:687–701, 2020.40) Liu, Y., Zhang, S., Ma, S. & Zhang, Q. Tests for regression coefficients in high dimensional partially linear models. Statistics and Probability Letters, 163: 108772-108777, 2020.41) Zhang, S., Fan, Y., Zhong, T. & Ma, S. Histopathological imaging features‑ versus molecular measurements‑based cancer prognosis modeling. Scientific Reports, 10:15030-15038, 2020.42) Sun, X., Zhang, S., Ma, R., Tao, Y., Zhu, Y., Yang, D. & Shi, Y. Natural speckle-based watermarking with random-like illuminated decoding. Optics Express, 31832-31843, 2020.43) Ren, M., Zhang, S. & Zhang, Q., Robust high-dimensional regression for data with anomalous responses, Annals of the Institute of Statistical Mathematics, https://doi.org/10.1007/s10463-020-00764-1., 2020.44) Ren, M., Zhang, S., Zhang, Q. & Ma, S., Gaussian graphical model-based heterogeneity analysis via penalized fusion, Biometrics, https://doi.org/10.1111/biom.13426., 2021.45) Ren, M., Zhang, S., Zhang, Q. & Ma, S., HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis, Bioinformatics, https://doi.org/10.1093/bioinformatics/btab134., 2021.46) Zhang, S., Hu, X., Luo, Z., Jiang, Yu., Sun, Y. & Ma, S., Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion, Statistics in Medicine, https://doi.org/10.1002/sim.9006., 2021. 47) Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S., Hierarchical cancer heterogeneity analysis based on histopathological imaging features, Biometrics, https://doi.org/10.1111/biom.13544., 2021.48) Ren, M., Zhang, S., Ma, S. & Zhang, Q., Gene-environment interaction identification via penalized robust divergence, Biometrical Journal, https://doi.org/10.1002/bimj.202000157., 2022.
科研活动
主持和参与了多项纵向和横向课题，包括国家自然科学基金青年基金、中国科学院大学校长基金、企业科研项目等。
工作经历
2010.6~ 中国科学院大学 教授
2005.6~2010.6 中国科学院大学 副教授
2002.7~2005.6 中国科学院大学 讲师
2013 中国科学院大学，网络信息中心.