Faculty
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-in?ated 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
RESEARCHPROJECTS 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