康晓宁，东北财经大学国际商学院、供应链管理研究院副教授，硕士生导师。本科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数据的分析、混合数据建模、半参数模型的统计推断、贝叶斯统计等。曾先后主持和参与辽宁省教育厅基金、国家自然科学基金、美国自然科学基金等项目。其研究成果已发表在国际统计协会(ISI)的旗舰期刊International Statistical Review, 美国质量协会的旗舰期刊Journal of Quality Technology, 以及Journal of Statistical Computation and Simulation, Journal of Applied Statistics 等国际期刊上。
教育背景
本科:大连理工大学,数学专业
硕士:大连理工大学,数学专业
博士:Virginia Tech,统计专业
科研成果或研究方向
研究方向
Large sparse inverse covariance matrix estimation and its application
High-dimensional data analysis and modeling
Bayesian hierarchical modeling
Semiparametric modeling and inference
期刊与专著
Kang X. and Deng X.* (2019). An improved modified Cholesky decomposition approach for precision matrix estimation. Journal of Statistical Computation and Simulation, accepted.
Kang X., Deng X.*, Tsui K. and Pourahmadi M. (2019). On variable ordination of modified Cholesky decomposition for estimating time-varying covariance matrices. International Statistical Review, accepted.
Kang X., Xie C. and Wang M.* (2019). A Cholesky-based estimation for large-dimensional covariance matrices. Journal of Applied Statistics, accepted.
Wang M., Zhao P. and Kang X.* (2018). Structure identification for varying coefficient models with measurement errors based on kernel smoothing. Statistical Papers, accepted.
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.
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.
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.
主持项目
高维协方差矩阵估计的理论研究，辽宁省教育厅项目(主持)
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，国家自科面上项目(参与)
Collaborative Research: Experimental Design and Analysis of Quantitative-Qualitative Responses in Manufacturing and Biomedical Systems, NSF (参与)
