Calculated based on number of publications stored in Pure and citations from Scopus
20162025

Research activity per year

Personal profile

Personal profile

Kong Xiangshun Professional title: Assistant professor E-mail: kongsunday@163.com; xskong@bit.edu.cn
In 2017, he graduated from the School of Mathematical Sciences of Peking University majoring in Statistics and entered the Beijing Institute of Technology. Experimental design (mainly for optimal design, space filling design, etc.), currently the deputy head of the Department of Statistics, teaching undergraduate and graduate experimental design courses, and hosting a National Natural Science Foundation.
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Research Interests

Experimental design (mainly engaged in the optimal design and space filling design related research)

Education

2008-2012 School of Mathematical Sciences, Nankai University, Bachelor of Basic Mathematics 2012-2017 PhD in Probability Theory and Mathematical Statistics, School of Mathematical Sciences, Peking University

Professional Experience

2017-present Assistant Professor, Department of Statistics, School of Mathematics and Statistics, Beijing Institute of Technology
2018-2020 Visiting Scholar, Haslam School of Business, Tennessee State University

Research Achievement

1. Yin, C., Ai, M., Chen, X., Kong, X. (2022). Empirical likelihood for generalized linear models with longitudinal data. Journal of Systems Science and Complexity, accepted on Jan 27, 2022.
2. Kong, X., Yuan, M. and Zheng, W. (2021). Approximate and exact designs for total effects. Annals of Statistics, 3(49): 1594–1625.
3. Kong, X. and Zheng, W. (2020). Design based incomplete U-statistics. Statistica Sinica, 3(32): 1593–1618.
4. Huang, Y., Kong, X. and Ai, M. (2019). Optimal designs for the Lasso in sparse linear models. Metrika, 83: 255–273.
5. Yu, J., Kong, X., Ai, M. and Tsui, K. L. (2018). Optimal designs for dose–response models with linear effects of covariates. Computational Statistics & Data Analysis, 127: 217–228.
6. Kong, X., Ai, M. and Tsui, K. L. (2018). Design for sequential follow-up experiments in computer emulations. Technometrics, 60(1): 61–69.
7. Kong, X., Ai, M. and Tsui, K. L. (2018). Flexible sliced designs for computer experiments. Annals of the Institute of Statistical Mathematics, 70(3): 631–646.

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