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Faculty > Professors > JIANG Xuejun

JIANG Xuejun

Associate Professor  

0755-88018687

  • Brief Biography
  • Research
  • Teaching
  • Published Works

CURRENTPOSITIONS:

Associate Professor, Department of Mathematics, Southern University of Science and Technology (SUSTech), Shenzhen, China. [CV]


Research Interests:

Quantile Regression

Parametric and nonparametric inference

High-dimensional data analysis

Statistics in Financial Econometrics

Bayesian analysis and its application


Professional Experience:

Associate professor, Department of Mathematics, 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


HONORand AWARDS:

Excellent Teacher Award, SUSTech, 2018. 

Excellent Teacher Honour, Shenzhen City, 2018.

Excellent Mentor Award, SUSTech, 2016

Peacock Plan Talent Programme, Shenzhen City, 2016


Selected Publication:

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

2.  Jiang, X.,  Fu, Y., Jiang, J., Li, J. (2018). Spatial Distribution of the Earthquake in Mainland China. Physica A: Staitsical Mechanics and its Application, online.

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

4.  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.

5. 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) 

6.  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.

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

8. 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)

9. 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

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

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

21. 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.   

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

23. 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.

24. 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 .

25. 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.

26. 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.

27. 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.

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

29. 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.

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

31. 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

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

33. 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]

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

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

36. 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]

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

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

39. 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. 

40. 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.

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


Manuscripts under revision: 

42 Tan, F., Jiang, X.*, Guo, X. and Zhu, L. (2019). Testing heteroscedasticity for regression models based on projections. Statistica Sinica, accepted. [PDF]

       43 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]


RESEARCHPROJECTS as PI:


1.  NSFC Award (General programme)

Grant Number: 11871263.

Project Name:Likelihood inference for high-dimensional parametric and semi-parametric models

Amount of Funding: RMB 550,000.00                   

Research period: 01/2019-12/2022


2.  NSFC Award (Youth programme)

Grant Number:11101432,

Project Name:Inference of DTARCH, GARCH and FARCH models based on Weighted Composite Quantile Regression

Amount of funding: RMB 210,000.00

Research period: 01/2012-12/2014  


3.  Peacock Plan Talent Programme

Project Name: Nonparametric Statistical Modelling for High Dimensional and Big Data and Its Applications

Grant Number: 20160721387C

Amount of funding: RMB 2,000,000.00  

02/2017-02/2022


4.  NSFC from Guangdong Province

Grant Number: 2017A030313012

Project Name:A dynamic Bayesian statistical study on the AIDS and other major epidemic diseases control

Amount of funding: RMB 100,000.00

Research period: 06/2016-06/2019


5.  NSFC from Guangdong Province

Grant Number: 2016A030313856

Project:A Study of Model Selection and Statistical diagnosis for Count Data

Amount of funding RMB: 100,000.00             

Research period: 06/2016-06/2019


6.  Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20170307110329106

Project Name:Study on risk prediction and dynamic prevention of AIDS epidemic in Shenzhen City

Amount of funding: RMB 300,000.00         

Research period: 06/2017-06/2019


7.  Enterprise Horizontal Research Programme

Grant Number: K1628Z015

Project Name:A quantitative trading system based on depth machine learning

Amount of funding: RMB 200,000.00                                

Research period: 08/2016-08/2018


RESEARCHPROJECTS as Co-PI:


8.  NSFC (General programme)

Grant Number: 1157116

Project Name:Research on Positioning Imaging Theory and Algorithms of Electromagnetic Inverse Scattering Problems

Amount of funding: RMB 550,000.00                                                 

Research period: 01/2016-12/2019


9.  Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20140509143748226

Project Name:Research on Relevant Theory and Algorithms of Inverse Scattering Problem

Amount of funding: RMB 300,000.00                                               

Research period: 01/2015-12/2016
























RESEARCH PROJECTS as PI:

1.     NSFC Award (General programme)

Grant Number: 11871263.

Project Name: Likelihood inference for high-dimensional parametric and semi-parametric models

Amount of Funding: RMB 550,000.00                   

Research period: 01/2019-12/2022


2.     NSFC Award (Youth programme)

Grant Number:11101432,

Project Name: Inference of DTARCH, GARCH and FARCH models based on Weighted Composite Quantile Regression

Amount of funding: RMB 210,000.00

Research period: 01/2012-12/2014  


3.     NSFC from Guangdong Province

Grant Number: 2017A030313012

Project Name: A dynamic Bayesian statistical study on the AIDS and other major epidemic diseases control

Amount of funding: RMB 100,000.00

Research period: 06/2016-06/2019


4.     NSFC from Guangdong Province

Grant Number: 2016A030313856

Project: A Study of Model Selection and Statistical diagnosis for Count Data

Amount of funding RMB: 100,000.00             

Research period: 06/2016-06/2019


5.     Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20170307110329106

Project Name: Study on risk prediction and dynamic prevention of AIDS epidemic in Shenzhen City

Amount of funding: RMB 300,000.00         

Research period: 06/2017-06/2019


6.     Enterprise Horizontal Research Programme

Grant Number: K1628Z015

Project Name: A quantitative trading system based on depth machine learning

Amount of funding: RMB 200,000.00                                

Research period: 08/2016-08/2018


RESEARCH PROJECTS as Co-PI:

7.   NSFC (General programme)

Grant Number: 1157116

Project Name: Research on Positioning Imaging Theory and Algorithms of Electromagnetic Inverse Scattering Problems

Amount of funding: RMB 550,000.00                                                 

Research period: 01/2016-12/2019


8.   Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20140509143748226

Project Name: Research on Relevant Theory and Algorithms of Inverse Scattering Problem

Amount of funding: RMB 300,000.00                                               

Research period: 01/2015-12/2016

Courses taughted

1. Multivariate Statistics Analysis (2018 Spring)

2. Econometrics (2018 Spring)

3. Time Series Analysis (2018  Fall)

4. Advanced Statistics (2018 Fall, postgraudates)

RESEARCH PROJECTS as PI:

1.     NSFC Award (General programme)

Grant Number: 11871263.

Project Name: Likelihood inference for high-dimensional parametric and semi-parametric models

Amount of Funding: RMB 550,000.00                   

Research period: 01/2019-12/2022


2.     NSFC Award (Youth programme)

Grant Number:11101432,

Project Name: Inference of DTARCH, GARCH and FARCH models based on Weighted Composite Quantile Regression

Amount of funding: RMB 210,000.00

Research period: 01/2012-12/2014  


3.     NSFC from Guangdong Province

Grant Number: 2017A030313012

Project Name: A dynamic Bayesian statistical study on the AIDS and other major epidemic diseases control

Amount of funding: RMB 100,000.00

Research period: 06/2016-06/2019


4.     NSFC from Guangdong Province

Grant Number: 2016A030313856

Project: A Study of Model Selection and Statistical diagnosis for Count Data

Amount of funding RMB: 100,000.00             

Research period: 06/2016-06/2019


5.     Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20170307110329106

Project Name: Study on risk prediction and dynamic prevention of AIDS epidemic in Shenzhen City

Amount of funding: RMB 300,000.00         

Research period: 06/2017-06/2019


6.     Enterprise Horizontal Research Programme

Grant Number: K1628Z015

Project Name: A quantitative trading system based on depth machine learning

Amount of funding: RMB 200,000.00                                

Research period: 08/2016-08/2018


RESEARCH PROJECTS as Co-PI:

7.   NSFC (General programme)

Grant Number: 1157116

Project Name: Research on Positioning Imaging Theory and Algorithms of Electromagnetic Inverse Scattering Problems

Amount of funding: RMB 550,000.00                                                 

Research period: 01/2016-12/2019


8.   Science and Technology Innovation Committee project from Shenzhen City

Grant Number: JCYJ20140509143748226

Project Name: Research on Relevant Theory and Algorithms of Inverse Scattering Problem

Amount of funding: RMB 300,000.00                                               

Research period: 01/2015-12/2016