JIANG Xuejun
Associate Professor

Research Interests:
◆ Statistics in Financial Econometrics
◆ Quantile Regression, Variable Selection
◆ Survival analysis
◆ Nonparametric regression


Professional Experience:
◆ 2013.07-Present, Tenure-Track Assistant Professor, Southern University of Science and Technology
◆ 2011.10-2013.07, Tenure-Track Assistant Professor, Dept. of. Mathematical Statistics and Financial Statistics, Zhongnan University of Economics and Law
◆ 2010.10-2011.09, Lecturer, Zhongnan University of Economics and Law, Dept. of. Mathematical and Quantitative Economics
◆ 2009.09-2010.09, Post-doctoral, Dept. of. Sta,The Chinese University of Hong Kong


Educational Background:
◆ P.H.D The Chinese University of Hong Kong
◆ Msc Yunnan University
◆ Bsc National University of Defense Technology


Selected Publication:

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

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

[3]  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.[PDF]

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

[5] 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.

[6] 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)

[7] 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.

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

[9] 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)

[10] 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

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] 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.

[20] 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.

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

[22] 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.

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

[24] 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.

[25] 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 .

[26] 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.

[27] 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.

[28] 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.

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

[30] 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.

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

[32] 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

[33] 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.

[34] 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]

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

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

[37] 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]

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

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

[40] 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.

[41] 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.

[42] 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.


Research Porjects 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


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