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康晓宁 副教授
研究方向:高维数据分析、贝叶斯分层模型、混合数据分析、半参数建模及推断
邮箱:kangxiaoning@dufe.edu.cn
职称:副教授
个人简历

康晓宁,统计学博士,副教授,硕士生导师。本科、硕士毕业于大连理工大学数学系,博士毕业于美国弗吉尼亚理工大学统计系。主要研究领域包括高维矩阵估计、混合数据建模、迁移学习、半参数模型的统计推断、贝叶斯统计等。曾先后主持和参与国家自然科学基金、教育部人文社科基金、辽宁省自然科学基金、辽宁省教育厅基金等项目。其研究成果已发表在Technometrics, Journal of Multivariate Analysis, IISE Transactions, International Statistical Review, Statistics in Medicine等国际知名期刊上。


代表性学术成果

1. Wang S., Xie C. and Kang X.* (2023). A novel robust estimation for high-dimensional precision matrices, Statistics in Medicine, in press.

2. Chen X., Kang X.#, Jin R. and Deng X. (2023) Bayesian sparse regression for mixed multi-responses with application to runtime metrics prediction in fog manufacturing, Technometrics, in press.

3. Cao Z., Kang X.# and Wang M. (2023). Doubly robust weighted composite quantile regression based on SCAD-L2, Canadian Journal of Statistics, in press.

4. Yang W. and Kang X.* (2023). An improved banded estimation for large covariance matrix, Communications in Statistics - Theory and Methods, 52(1), 141-155.  

5. Wang M., Kang X.# and Tian G. (2022). Modified adaptive group Lasso for high-dimensional varying coefficient models, Communications in Statistics - Simulation and Computation, 51(11), 6495-6510.

6. Kang X., Kang L, Chen W. and Deng X. (2022). A generative modeling approach to modeling data with qualitative and quantitative responses, Journal of Multivariate Analysis, 190, 104952.

7. Li C., Yang M., Wang M., Kang H. and Kang X.* (2021). A Cholesky-based sparse covariance estimation with an application to genes data, Journal of Biopharmaceutical Statistics, 31(5), 603-616.

8. Kang X., Ranganathan S., Kang L., Gohlke J. and Deng X. (2021). Bayesian auxiliary variable model for birth records data with qualitative and quantitative responses, Journal of Statistical Computation and Simulation, 91(16), 3283-3303.

9. Kang X. and Wang M. (2021). Ensemble sparse estimation of covariance matrix for exploring genetic disease data, Computational Statistics & Data Analysis, 159, 107220.

10. Kang X. and Deng X. (2021). On variable ordination of Cholesky-based estimation for a sparse covariance matrix, Canadian Journal of Statistics, 49(2), 283-310.

11. Kang X., Chen X., Jin R., Wu H. and Deng X. (2021). Multivariate regression of mixed responses for evaluation of visualization designs. IISE Transactions, 53(3), 313-325.

12. Kang X., Deng X., Tsui K. and Pourahmadi M. (2020). On variable ordination of modified Cholesky decomposition for estimating time-varying covariance matrices. International Statistical Review, 88(3), 616-641.

13. Kang X. and Deng X. (2020). Design and analysis of computer experiments with quantitative and qualitative inputs: a selective review. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 10(3), e1358.

14. Kang X. and Deng X. (2020). An improved modified Cholesky decomposition approach for precision matrix estimation. Journal of Statistical Computation and Simulation, 90(3), 443-464.

15. Kang X., Xie C. and Wang M. (2020). A Cholesky-based estimation for large-dimensional covariance matrices. Journal of Applied Statistics, 47(6), 1017-1030. 

16. Wang M., Zhao P. and Kang X.* (2020). Structure identification for varying coefficient models with measurement errors based on kernel smoothing. Statistical Papers, 61(5), 1841-1857.

17. Kang L., Kang X.#, Deng X. and Jin R. (2018). A Bayesian hierarchical model for quantitative and qualitative responses. Journal of Quality Technology, 50(3), 290-308.

18. Zheng H., Tsui K., Kang X. and Deng X. (2017). Cholesky-based model averaging for covariance matrix estimation. Statistical Theory and Related Fields, 1(1), 48-58.

19. Wang X., Song L. and Kang X. (2014). Profile likelihood inferences on the partially linear model with a diverging number of parameters. Communications in Statistics - Theory and Methods, 43(1), 13-27.

主要科研课题

1.辽宁省教育厅基本科研项目,基于模型平均的矩阵估计和判别分析方法研究,主持。

2.辽宁省自然科学基金项目,乔列斯基分解下的高维图模型估计方法研究,主持。

3.教育部基金项目,高维数据背景下协方差阵及其逆矩阵估计的理论研究和应用,主持。

4.辽宁省教育厅项目,高维协方差矩阵估计的理论研究,主持。

5.国家自然科学基金重点项目,数智时代下的供应链管理与模式创新,参与。

6.国家自然科学基金面上项目,多渠道多产品环境下三类随机动态库存系统最优策略的研究,参与。


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  • 电子邮箱:isca_2016@dufe.edu.cn
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