个人主页：http://faculty.sustc.edu.cn/profiles/jiangxj

个人简介：

蒋学军，2009年博士毕业于香港中文大学统计学系；2009年09月-2010年09月在香港中文大学从事博士后研究，2010年7月至2013年6月历任中南财经政法大学统计与数学学院讲师，副教授,研究生导师。2013年07月加入南方科技大学，主持有国家自然科学基金（青年，面上），广东省自然科学基金(2项)，深圳市科创委项目和技术委托开发项目，广东省本科教学改革项目等。已在统计学国际主流期刊和相关金融、经济等交叉学科期刊上发表SCI&SSCI论文40余篇。

研究领域：

• 分位数回归

• 参数和非参数推断

• 高维数据降维与分析

• 金融与计量经济统计

• 贝叶斯分析及应用

工作经历：

2019.07-现在，副教授，统计与数据科学系，南方科技大学

2015.07-2019.06，Tenure-Track助理教授，数学系，南方科技大学

2013.07-2015.06，Tenure-Track助理教授，金融数学与金融工程系，南方科技大学

2011.09-2014.01，班主任&指导教师，中南财经政法大学，2011级EMBA深圳班

2011.10-2013.07，副教授&教研室主任，硕士生导师，数理统计与金融统计系，中南财经政法大学

2010.10-2011.09，讲师，数学与数量经济系，中南财经政法大学

2009.09-2010.09，博士后，香港中文大学统计学系

教育背景：

博士，香港中文大学，香港, 2009

硕士，云南大学，昆明, 2006

本科，国防科技大学，长沙, 2000

所获荣誉：

• 南方科技大学 “优秀导师奖”，2020

• 深圳市优秀教师，2018

• 南方科技大学 “杰出教学奖”，2018

• 南方科技大学 “优秀导师奖”，2016

• 深圳市海外高层次人才“孔雀计划”入选者

教材 :

Tian Guolian and Jiang Xuejun.(2021). Mathematical Statistic数理统计（英文版）. 科学出版社, China.

发表论文：

1. Hong S., Jiang, J., Jiang X. and Xiao Z. (2020). Unifying inference for semiparametric regression. The Econometrics Journal. Accepted.

2. Tan, F., Jiang, X., Guo, X. and Zhu, L. (2021). Testing heteroscedasticity for regression models based on projections. Statistica Sinica, 31(2).

3. Jiang, X., Liu W. And Zhang, B. (2021). A note on the prediction of frailties with misspecified shared frailty models. Journal of Statistical Computation  and Simulation, 91(2), 219-241.

4. Guo, G., Su, Y. and Jiang, X. (2020). A partitioned quasi-likelihood for distributed statistical inference. Computational Statistics, 35, 1577–1596.

5. Guo, X., Jiang. X., Zhang, S. and Zhu, L. (2020). Pairwise distance-based heteroscedasticity for regressions. Science China-Mathematics, 63(1).

6. Jiang, X., Li, Y., Yang, A. and Zhou, R. (2020). Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk. Empirical Economics, 58(5). Q3

7. Jiang, X., Fu, Y., Jiang, J., Li, J. (2019). Spatial Distribution of the Earthquake in Mainland China. Physica A: Staitsical Mechanics and its Application, 530(15). Q2

8. 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.

9. 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) 

10. 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.

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

12. 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)

13. 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

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

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.

21. 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.

22. 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.

23. 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.

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

25. 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.   

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

27. 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.

28. 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 .

29. 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.

30. 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.

31. 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.

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

33. 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.

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

35. 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

36. 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.

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

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

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

40. 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.

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

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

43. 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. 

44. 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.

45. 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.

在审论文（Revision）：

1.  Wang, H., Jiang, X.*, Zhou, M. and Jiang  J. (2020). Variable selection in distributed sparse regression under memory constraints.

2. Jiang J. and Jiang X. (2020). Nonparametric Inference for Partly Linear Additive Cox Models based on Polynomial Spline Estimation.

科研项目（主持）：

1. 国家自然科学基金面上项目，高维参数和半参数模型下似然推断，项目编号11871263，55万，01/2019-12/2022，主持

2. 国家自然科学基金青年项目，基于加权复合分位数回归的双门限ARCH，广义ARCH, 及函数系数ARCH模型的推断. 项目编号:11101432, 21万，01/2012-04/2015，结题

3. 广东省自然科学基金项目，广东省艾滋病等重大流行病防治的动态贝叶斯统计研究，项目编号2017A030313012，10万，2017/05-2020/05，主持

4. 广东省自然科学基金项目，计数数据模型选择与统计诊断研究，项目编号2016A030313856, 经费 10 万, 06/2016-06/2019，结题

5. 深圳市科创委项目，深圳市艾滋病流行情况风险预测及动态防治的研究，项目编号JCYJ20170307110329106，经费30万,06/2017.06-06/2019，结题

6. 深圳市技术委托开发项目，基于深度机器学习的量化交易系统， 项目编号：K1628Z015，经费20 万，08/2016-08/201，结题

任教课程：

Multivariate Statistics Analysis (2018 Spring)

Econometrics (2018 Spring)  

Time Series Analysis (2018 Fall)

Advanced Statistics  (2018 Fall, postgraudates)