Associate Professor Department of Statistics and Data Science

Personal Profile

Professional Experience

Associate professor,  Department of Statistics and Data Science, SUSTech, Shenzhen. 07/2019-Present

Assistant professor, Department of Mathematics, SUSTech, Shenzhen. 07/2015-06/2019

Assistant professor, Department of Financial Mathematics and Financial Engineering, SUSTech, Shenzhen. 07/2013-06/2015

Associate professor, Postgraduate tutor, School of Statistics and Mathematics, Zhongnan University of Economics and Law,  Wuhan. 10/2010-06/2013

Assistant Professor, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan. 10/2010-09/2011

Postdoctoral Fellow, The Chinese University of Hong Kong Hong Kong.  09/2009-09/2010


Educational Background:

P.H.D The Chinese University of Hong Kong

Msc Yunnan University

Bsc National University of Defense Technology


Honor and Awards:

Excellent Teacher Award, SUSTech, 2018. 

Excellent Teacher Honour, Shenzhen City, 2018.

Excellent Mentor Award, SUSTech, 2016

Peacock Plan Talent Programme, Shenzhen City, 2016




· Quantile Regression

· Parametric and nonparametric inference

· High-dimensional data analysis

· Statistics in Financial Econometrics

· Bayesian analysis and its application


Multivariate Statistics Analysis (2018 Spring)

Econometrics (2018 Spring)

Time Series Analysis (2018 Fall)

Advanced Statistics (2018 Fall, postgraudates)

Publications Read More

· Tan, F., Jiang. X., Guo, X. and Zhu, L. (2019). Testing heteroscedasticity for regression models based on projections. Statistica Sinica, online.

· Guo, X., Jiang. X., Zhang, S. and Zhu, L. (2019). Pairwise distance-based heteroscedasticity for regressions. Science China- Mathematics, online.

· Jiang, X., Fu, Y., Jiang, J., Li, J. (2019). Spatial Distribution of the Earthquake in Mainland China. Physica A: Staitsical Mechanics and its Application, online.

· Jiang, X., Li, Y. , Yang, A. and Zhou, R. (2018). Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk. Empirical Economics, online.

· Lin, H., Jiang, X., Liang, H. and Zhang, W. (2018). Reduced rank modelling for functional regression with functional responses. Journal of multivariate analysis,169,205-217.

· Jiang, X. and Fu, Y. (2018). Measuring the Benefits of Development Strategy of “The 21st CenturyMaritime Silk Road” via Intervention Analysis Approach: Evidence from China and Neighboring Countries in Southeast Asian. Panoeconomicus,65(5)

· Xia, T., Jiang, J. and Jiang, X. (2018). Local influence for quasi-likelhood nonlinear models with random effects. Journal of Probability and Statistics. Vol 2018. 7.

· Li, J., Jiang, J., Jiang, X. and Liu, L.(2018). Risk-adjusted Monitoring of Surgical Performance. PLOSONE, 13(8), 1-13

· Zhao, W., Jiang, X. and Liang H. (2018). A Principal Varying-Coefficient Model for Quantile Regression: Joint Variable Selection and Dimension Reduction. Computational Statistics and Data Analysis,127, 269-280. (2018,11)

· Yang, A., Jiang, X., Shu, L., Lin, J. (2018). Sparse Bayesian Kernel Multinomial Probit Regression Model for High-dimensional Data Classification. Communication in statistics-theory and methods. Online

· Tian, G., Liu, Y., Tang, M. and Jiang, X. (2018). Type I multivariate zero-truncated/adjusted distributions with applications. Journal of computational and applied mathematics,344(15), 132-153.

· Jiang X., Guo, X., Zhang, N., Wang, B. and Zhang, B.* (2018). Robust multivariate nonparametric tests for detection of two- sample location shift in clinical trials. PLOSONE,13(4), 1-20.

· Yan A., Liang H., Jiang X. and Liu P. (2018). Sparse Bayesian variable selection for classifying high-dimensional data. Statistics and its interface,11(2), 385-395.

· Tian, G., Zhang, C. and Jiang, X. (2018). Valid statistical inference methods for a case-control study with missing data. Statistical Methods in Medical Research,27(4), 1001-1023.

· Xia T., Jiang X. and Wang X. (2018). Asymptotic properties of approximate maximum quasi-likelihood estimator in quasi- likelihood nonlinear models with random effects. Communication in Statistics,47, 1-12.

· Song, X. Kang, K. Ouyang, M., Jiang, X. and Cai. J. (2018). Bayesian Analysis of Semiparametric Hidden Markov Models with Latent Variables. Structural Equation Modeling: A Multidisciplinary Journal.25(1), 1-20.

· Li J., Liang, H., Jiang, X. and Song, X. (2018). Estimation and Testing for Time-varying Quantile Single-index Models with Longitudinal Data. Computational Statistics and Data Analysis,118, 66-83.

· Feng, K. and Jiang, X. (2017). Variational approach to shape derivatives for elasto-acousticcoupled scattering fields and an application with random interfaces. Journal of Mathematical Analysis and Application,456, 686-704.

· Jiang, J., Jiang. X., Li, J. Li, Y and Yan, W. (2017). Spatial Quantile Estimation of Multivariate Threshold Time Series Models. Physical A: Statistical Mechanics and Its Application,486,772-781.

· Guo, X., Jiang, X. and Wong, W. (2017). Stochastic Dominance and Omega Ratio: Measures to Examine Market Efficiency and Anomaly. Economies, 5(38),1-16.

· Tian, X., Jiang, X., and Wang, X. (2017). Diagnostics for quasi-likelihood nonliear models. Communication in Statistics-Theory and Methods,47(16), 8836-8851.

· Jiang, X., Tian, X. and Wang, X. (2017). Asymptotic properties of maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression. Communication in Statistics-Theory and Methods,46(13), 6229-6239. 22.

· Niu, C. and Jiang, X. (2017). Statistical inference for a novel health inequality index. Theoretical Economics Letters,7, 251-262.

· Yang, A, Jiang, X., Xiang, L and Lin J. (2017). Sparse Bayesian Variable Selection in Multinomial Probit Regression Model with Application to High-dimensional Data Classification. Communication in Statistics-Theory and Methods.46(12), 6137-6150.

· Yang, A., Jiang, X., Shu, L. and Lin J. (2017). Bayesian Variable Selection with Sparse and Correlation Priors for High-dimensional Data Analysis. Computational Statistics,32, 127-143 .

· Huang, X., TIAN, G., Zhang, C. and Jiang, X. (2017). Type I multivariate zero-inflated generalized Poisson distribution with applications. Statistics and Its Interface,10(2), 291-311.

· Yang, A., Jiang, X., Liu, P. and Lin J. (2016). Sparse bayesian multinomial probit regression model with correlation prior for High-dimensional data Classification. Statistics and probability letters,119,241-247.

· Jiang, X., Li, J., Xia, T and Wang, Y. (2016) Robust and efficient estimation with weighted composite quantile regression. Physical A: Statistical Mechanics and its Applications,457, 413-423.

· Jiang, X., Song, X. and Xiong, Z. (2016) Robust and efficient estimation of GARCH models. Journal of Testing and Evaluation,44(5), 1-23.

· Li, H., Tian, G., Jiang, X. and Tang, N. (2016). Testing hypothesis for a simple ordering in incomplete contingency tables. Computational Statistics and Data Analysis,99,25-37.

· Li, Y., Tang, N. and Jiang, X. (2016). Bayesian Approaches for Analyzing Earthquake Catastrophic Risk. Insurance: Mathematics and Economics, 68, 110-119.

· Xia, T., Jiang, X. and Wang, X. (2015). Strong consistency of the maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression. Statistics & Probability letters,103, 37-45

· Xia, T., Wang, X. and Jiang, X. (2014). Asymptotic properties of maximum quasi-likelihood estimator in quasi likelihood nonlinear models with misspecified variance function. Statistics,48(4), 778-786.

· Song, X., Cai, J., Feng, X. and Jiang, X. (2014). Bayesian Analysis of Functional-Coefficient Autoregressive Heteroscedastic Model. Baysian Analaysis,9(2), P1-26.[PDF]

· Jiang, X., Tian, T. and Xie, D. (2014). Weighted type of quantile regression and its application. IMECS2014, II, 818-822.

· Jiang J, Jiang, X. and Song X(2014) Weighted composite quantile regression estimation of DTARCH models. The Econometrics Journal, 17(1),1-23 [PDF]

· Jiang, X., Jiang, J. and Song, X. (2012.). Oracle model selection for nonlinear models based on weighted composite nonlinear quantile regression. Statistica Sinica,22(4), 1479-1506.[PDF]

· Jiang, J. and Jiang, X. (2011). Inference for partly linear additive COX models. Statistica Sinica,21(2),901-921.[PDF]

· Jiang, X., Jiang, J. and Liu, Y. (2011). Nonparameteric regression under double-sampling designs. Journal of Systems Science and Complexity,24, 1-9.

· Xia, T., Wang, X. and Jiang, X. (2010). Asymptotic properties of the MLE in nonlinear reproductive dispersion models with stochastic regressors. Communication in Statistics,Theory and Methods,39, 2800-2810.

· Jiang, J., Marron, J.S. and Jiang, X.(2009). Robust Centroid Quantile Based Classification for High Dimension Low Sample Size Data. Journal of Statistical Planning and Inference,139(8), 2571-2580.

· Jiang, J., Zhou, H.,Jiang, X. and Peng, J. (2007). Generalized likelihood ratio tests for the structures of semiparametric additive models. The Canadian Journal of Statistics,35(3), 381-398.


· Jiang J. and Jiang X.* (2019). Nonparametric Inference for Partly Linear Additive Cox Models based on Polynomial Spline Estimation. Journal of the Americal Statistical Association, revision.[PDF]

News More

  • SUSTech Launches New Dept of Statistics and Data Science

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The Department of Statistics and Data Science was established in April 2019. As a newly established department, we currently have 8 faculty members including 1 Chair Professor, 2 Professors, 2 Associate Professors , 2 Tenure-track Assistant Professors and 1 Visiting Assistant Professor and anticipant to rapidly expand to a team of more than 20 members. All faculty members of the department have extensive overseas study or work experiences. One member is an invited speaker at the International Congress of Mathematicians, an IMS Medallion Lecturer and a winner of the prestigious State Natural Science Award (2nd class). The department has 2 research directions, Statistics and Data Science, which cover a broad array of research areas including Biostatistics, Financial Statistics, Experiment Design, Clinical Trials, High Dimensional Data, Time series, Probability Theory, Data Science and Big Data Technology.


At present, the department has an undergraduate program as well as two graduate programs(M.Phil and PhD). The department is in the process of developing a Major Program in Data Science and Big Data Technology. In 2019, the first batch of 38 undergraduate students in Statistics received the bachelor degree from SUSTech. 5 M.Phil students in Statistics also graduated in the same year. There are currently 9 M.Phil students, 7 PhD students in Statistics and 2 Postdoctoral Fellows. As the department grows in size and strength, the number of graduate students is expected to rise substantially from the current level and the areas of graduate studies will also expand to include Data Science and Big Data Technology.


This is the age of big data which offers exciting opportunities to researchers in Statistics and Data Science. Our department is expected to grow rapidly in the coming years. We invite outstanding scholars to join us in our journey to become a first-class department in Statistics and Data Science. We also welcome brilliant undergraduate and graduate students to apply for our programs. At the same time, we actively recruit excellent postdoctoral candidates.


The Department of Statistics and Data Science:

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Room 522, Hui Yuan 3#

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