Associate Professor
Department of Statistics
School of Mathematical Sciences
Soochow University
1 Shi-Zi Street, Suzhou, 215006, Jiangsu Province, China
Email: xuejunma@suda.edu.cn

Research interests

Massive Data Analysis, Statistical Computing, High Dimensional Data Analysis, Empirical Likelihood and Its Application.

Working experience

  • Associate Professor, Soochow University, 2019 - present..
  • Research Fellow, National University of Singapore, 2017 - 2019.
  • Postdoctor, Beijing University of Technology, 2016 - 2017.

Education

  • Ph.D. Renmin University of China. 2016 .
  • M.S. North China Electric Power University. 2012.
  • B.S. Fuyang Teachers College. 2009.

Publications

  1. Chao, Y., Huang, L. Ma, X. , & Sun, J. (2023) Optimal subsampling for modal regression in massive data. Metrika. accepted
  2. Shen, L. Chao, Y., & Ma, X. (2023). Distributed quantile regression in decentralized optimization. Information Sciences, 643, 119259.
  3. Ma, X. , & Zhang, P. (2023). Quantile regression for compositional covariates. Communications in Statistics-Simulation and Computation, 52(3),658-668.
  4. Ma, X. , Du, Y., & Wang, J. (2022). Model detection and variable selection for mode varying coefficient model. Statistical Methods & Applications. 31, 321–341
  5. Ma, X. , Wang, S & Zhou, W. (2022). Statistical inference in massive datasets by empirical likelihood. Computational Statistics
  6. Ma, X. , Du, Y., & Wang, J. (2022). Model detection and variable selection for mode varying coefficient model. Statistical Methods & Applications
  7. Ma, X. , & Zhang, P. (2022). Quantile regression for compositional covariates. Communications in Statistics-Simulation and Computation
  8. Liu, P., Ma, X., & Zhou, W. (2022). High order conditional distance covariance with conditional mutual independence. Probability in the Engineering and Informational Sciences, 36(1), 126-143.
  9. Chao, Yue, Ma, X. , Li, Y, & Huang, L. (2022). A penalized estimation for the Cox model with ordinal multinomial covariates. Journal of Statistical Computation and Simulation
  10. Ma, X. , Wang, S., & Zhou, W. (2021). Testing multivariate quantile by empirical likelihood. Journal of Multivariate Analysis, 182, 104705.
  11. Chen, X., Ma, X., & Zhou, W. (2020). Kernel density regression. Journal of Statistical Planning and Inference, 205, 318-329.
  12. Wang, L, Ma, X. & Zhang, J. (2020). Feature screening for ultrahigh-dimensional additive logistic models. Journal of Statistical Planning and Inference. 205, 306-317.
  13. Chen, X, Ma, X. & Zhou, W. (2020). Kernel density regression. Journal of Statistical Planning and Inference. 205, 318-329
  14. Li, X., Ma, X. & Zhang, J. (2018). Conditional quantile correlation screening procedure for ultrahigh-dimensional varying coefficient models. Journal of Statistical Planning and Inference, 197, 69-92.
  15. Chen, Y., Ma, X. & Zhou, J. (2018). Variable selection for mode regression. Journal of Applied Statistics 45(6): 1077–1084.
  16. Chen, X., Ma, X. Wang, X. & Zhang, J. (2017). Efficient feature screening for ultrahigh-dimensional varying coefficient models. Statistics and Its Interface, 10(3), 407-412.
  17. Li, X., Ma, X. & Zhang, J. (2017). Robust feature screening for varying coefficient models via quantile partial correlation. Metrika, 80(1): 17–49.
  18. Ma, X. , Chen, X. & Zhang, J. (2017). Fast robust feature screening for ultrahigh-dimensional varying coefficient models. Journal of Statistical Computation and Simulation, 87(4), 724-732.
  19. Ma, X. & Zhang, J. (2016). Robust model-free feature screening via quantile correlation. Journal of Multivariate Analysis, 143, 472-480. R package QCSIS .
  20. Ma, X. & Zhang, J. (2016). A new variable selection approach for varying coefficient models. Metrika, 79(1), 59-72.
  21. Ma, X., He, X.& Shi, X. (2016). A variant of K nearest neighbor quantile regression. Journal of Applied Statistics, 43(3), 526-537.
  22. Ma, X. & He F. (2014). Power weighted quantile regression and its application. Journal of Data Science, 12(3): 535-544.