张庆昭

副教授

中科院 概率与数理统计博士

电话:0592-2180502

电子邮件:qzzhang at xmu.edu.cn

办公室:经济楼B503

Office Hours:每周一上午9:30-11:30

个人主页:


个人简介 研究成果 研究项目

工作经历
Associate Professor at  Department of Statistics, School of Economics and The Wang Yanan Institute for Studies in Economics, September 2016-

Postdoctoral Associate at Yale School of Public Health, Department of Biostatistics, August 2015-August 2016
Research Assistant at Department of Applied Mathematics, The HongKong Polytechnic University, July 2015
Postdoctoral Associate at School of Mathematics, University of Chinese Academy of Sciences, June 2013-September 2016
 
 
教育背景
Ph.D. in Probability and Mathematical Statistics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 2008-2013; 
B.S. in Mathematics and Applied Mathematics, Huazhong University of Science & Technology, 2004-2008
 
研究兴趣
Semiparametrics, High-dimensional data analysis, Empirical likelihood, Robust statistics, Statistical Machine Learning.
 
教学
Advanced Probability Theory, Multivariate Analysis

 

  1.  Ren, M., Zhang, S., Zhang, Q.* and Ma, S. *(2022). Gaussian Graphical Model-based Heterogeneity Analysis via Penalized Fusion.  Biometrics.
  2. Fang, K., Ren, R,  Zhang, Q. and Ma, S. *(2022) iSFun: an R package for integrative dimension reduction analysis, Bioinformatics, 38(11),3134–3135.
  3. Fang, K., Chen, Y., Ma, S. and Zhang, Q.*(2022). Biclustering Analysis of Functionals via Penalized Fusion.  Journal of Multivariate Analysis, 189, 104874. 
  4. Ren, M., Zhang, S., Ma, S. and Zhang, Q.*(2022). Gene-environment interactions identification via penalized robust divergence. Biometrical Journal, 64(3), 461-480. 
  5. He, B., Zhong, T., Huang, J., Liu, Y., Zhang, Q.* and Ma, S.*(2021). Histopathological Imaging-based Cancer Heterogeneity Analysis via Penalized Fusion with Model Averaging. Biometrics, 77(4), 1397-1408.
  6. Zhang, T., Li, Z., Liu, A. and Zhang, Q.* (2021). Estimation of Partial Derivative Functionals with an Application to Human Mortality Data Analysis. Science China Mathematics, 64(9), 2117--2140.
  7. Zhang, Q., Ma, S. and Huang, Y.*(2021). Promote sign consistency in the joint estimation of precision matrices. Computational Statistics & Data Analysis, 159, 107210.
  8. Ren, M., Zhang, S., Zhang, Q. and Ma, S. *(2021). HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis.  Bioinformatics, 37(18), 3073–3074.
  9. Ren, M., Zhang, S. and Zhang, Q.*(2021). Robust high dimensional variable selection for discrete response model with mislabeled data. Annals of the Institute of Statistical Mathematics, 73, 703--736. 
  10. Lai, P., Wang, F, Zhu, T. and Zhang, Q.* (2021). Model identification and selection for the single-index varying coefficient models. Annals of the Institute of Statistical Mathematics, 73, 457–480.
  11. Wu, M., Zhang, Q. and Ma, S.*(2020). Structured Gene-Environment Interaction Analysis. Biometrics, 76(1), 23-35. 
  12. Fan, X., Fang, K., Ma, S. and Zhang, Q.*(2020). Integrating Approximate Single Factor Graphical Models. Statistics in Medicine, 39(2), 146-155. 
  13. Zhang, X., Zhang, Q., Wang, X., Ma, S. and Fang, K.*(2020). Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers. Statistics in Medicine, 39(7), 955-967. 
  14. Fan, X., Fang, K., Ma, S., Wang, S. and Zhang, Q.*(2019). Assisted Graphical Model for Gene Expression Data Analysis. Statistics in Medicine, 38, 2364-2380.
  15. Chai, H., Zhang, Q., Huang, J. and Ma, S.*(2019). Inference for Low Dimensional Covariates in a High-Dimensional Accelerated Failure Time Model. Statistica Sinica, 29, 877-894.
  16. Wu, C., Zhang, Q., Jiang, Y. and Ma, S.*(2018). Robust Network-based Analysis of the Associations between (Epi)Genetic Measurements. Journal of Multivariate Analysis, 168, 119-130.
  17. Fang, K., Fan, X., Zhang, Q. and Ma, S.*(2018). Integrative Sparse Principal Component Analysis. Journal of Multivariate Analysis, 166, 1-16.
  18. Bao, F., Deng, Y., Du, M., Ren, Z., Zhang, Q., Zhao, Y., Suo, J., Zhang, Z., Wang, M.* and Dai, Q. (2018) Probabilistic natural mapping of gene-level tests for genome-wide association studies. Briefings in Bioinformatics, 19(4), 545-553.
  19. Huang, Y., Zhang, Q., Zhang, S., Huang, J. and Ma, S.* (2017) Promoting similarity of sparsity structures in integrative analysis with penalization. Journal of the American Statistical Association, 112, 342-350.
  20. Sun, Z., Chen, F., Zhou, X. and Zhang, Q.* (2017). Improved model checking methods for parametric models with responses missing at random. Journal of Multivariate Analysis, 154, 147-161.
  21. Zhang, Q., Duan, X. and Ma, S.* (2017). Focused Information Criterion and Model Average Under Generalized Rank Regression. Statistics & Probability letters, 122, 11-19.
  22. Zang, Y., Zhang, S., Li, Q. and Zhang, Q.* (2016). Jackknife empirical likelihood test for high-dimensional regression coefficients.  Computational Statistics & Data Analysis, 94, 302-316. 
  23. Lai, P., Zhang, Q.*, Lian, H. and Wang, Q. (2016). Efficient estimation for the heteroscedastic single-index varying coefficient models. Statistics & Probability letters, 110, 84-93.
  24. Zhang, Q., Zhang, S., Liu, J., Huang, J. and Ma, S.* (2016). Penalized integrative analysis under the accelerated failure time model.  Statistica Sinica, 26, 493-508.
  25. Zhang, T., Zhang, Q. and Wang, Q.* (2014). Model detection for functional polynomial regression. Computational Statistics & Data Analysis, 70, 183-197.
  26. Zhang, Q., Li, D.* and Wang, H. (2013). A note on tail dependence regression. Journal of Multivariate Analysis, 120, 163-172.
  27. Zhang, Q. and Wang, Q.* (2013). Local least absolute relative error estimating approach for partially linear multiplicative model. Statistica Sinica, 23, 1091-1116.
  28. (* 通讯作者)

    多源高维数据的整合分析方法与理论,国家自然科学面上基金(11971404), 2020.01-2023.12

    带顺序结构的高维交互效应模型的稳健推断方法,教育部人文社会科学研究青年基金 (19YJC910010),2019-2021
    高维异质多数据集整合方法研究,中央高校业务费(20720171064), 2017.01-2019.12
    基于分位数回归的高维数据降维及变量选择研究,国家自然科学青年基金(11401561),2015.01-2017.12
    基于中心分位数子空间的充分降维问题研究,中国博士后科学基金二等资助(2014M550799),2014.03-2016.06