师资

蒋学军
副教授
0755-88018687
jiangxj@sustech.edu.cn

蒋学军,副教授。2009年获香港中文大学统计学博士学位, 09年09月-10年10月在香港中文大学统计学系从事博士后研究,10年7月-13年6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。13年07月加入南方科技大学,获国家自然科学基金(青年,面上)、广东省自然科学基金(2项)、深圳市科创委项目、深圳市技术委托开发项目、广东省本科高校金融学类教改项目等。主要科研方向为数理统计、金融统计与计量,已发表SCI&SSCI期刊论文近40篇。

 

研究领域:
◆金融与计量经济统计
◆分位数回归
◆高维数据降维
◆生存分析
◆贝叶斯分析

 

工作经历:

2019.07-present,  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:

○ 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

 

代表文章:

[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.

 

Publication (In Chinese)
1. (2016). 基于MCMC抽样的金融贝叶斯半参数GARCH模型研究,数理统计与管理. To appear. 杨爱军,蒋学军,林金官,林晓星
2. (2006). The M-estimate of the local linear regression with variable bandwiths. Journal of Yunnan University, 28 (1), 12-15. 蒋学军,夏天,唐年胜

 

科研项目:

  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, 主持
  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, 主持
  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,主持
  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 万,08/2016-08/2018,主持
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