Junfeng Shang

Professor and Chair

Phone: 419-372-7457
Email: jshang@bgsu.edu
Address: Office: 458 Mitchell B. McLeod Hall
Department of Mathematics and Statistics
Bowling Green State University
Bowling Green, OH 43403-0206

Research Interests

• Mixed Models and Generalized Linear Models
• Model Selection and Modeling Diagnostics
• Multiple Comparison Procedures
• Bayesian Analysis
• Biostatistics

Education

• Ph.D., Statistics, University of Missouri, Columbia, 2005

Selected Publications

Ki, M. and Shang, J. (2024). Prediction of minimum wages for countries with random forests and neutral networks. Data Science in Finance and Economics, 4(2), 309-332.  

Gautam, J., Ebersole, W., Brigham, R., Shang, J., Vázquez-Ortega, A., and Xu, Z. (2024). Effects of Lake Erie dredged material on microbiomes in a farm soil of Northwestern Ohio. Journal of Environmental Quality, 1-11.   

Hapuhinna, N. and Shang, J. (2024). A Bootstrap method for estimation in linear mixed models with heteroscedasticity. Communications in Statistics-Theory and Methods. Communications in Statistics-Theory and Methods, 53 (11), 4012-4036. 

Ge, W. and Shang, J. (2024). Bootstrap-adjusted quasi-likelihood information criteria for mixed model selection.  Journal of Applied Statistics, 51:4, 621-645. 

Jiang, J. and Shang, J. (2023). Feature screening for high-dimensional variable selection in generalized linear models. Entropy (Basel), 25(6):851.

Alabiso, A. and Shang, J. (2023). High-dimensional linear mixed model selection by partial correlation. Communications in Statistics-Theory and Methods, 52(18), 6355-6380.  

Atutey, O. and Shang, J. (2022). Linear mixed model selection via minimum approximated information criterion. Communications in Statistics-Simulation and Computation.  

Lee, Y. and Shang, J. (2022). Estimation and selection in linear mixed models with missing data under compound symmetric structure. Journal of Applied Statistics, 49 (15), 4003-4027.  

Xiong, J. and Shang, J. (2021). A penalized approach to mixed model selection via cross-validation. Communications in Statistics-Theory and Methods, 50, 2481-2507.

Switlyk, V. and Shang, J. (2019). Comparison of models for the prediction of the stock price. Journal of Mathematics and Statistics, 15, 233-249.

Pan, J. and Shang, J. (2018). A simultaneous variable selection methodology for linear mixed models. Journal of Statistical Computation and Simulation, 88(17), 3323-3337.

Pan, J. and Shang, J. (2018). Adaptive Lasso for linear mixed model selection via profile loglikelihood. Communications in Statistics-Theory and Methods, 47(8), 1882-1900.

Pan, J. and Shang, J. (2017). Prediction of colon cancer expense via adaptive penalized mixed model selection. Mathematica in Engineering, Science and Aerospace (MESA), 8(2), 253-264.

Shang, J. (2016). A diagnostic of influential cases based on the Information Complexity Criteria in generalized linear mixed models. Communications in Statistics-Theory and Methods, 45 (13), 3751-3760.

Wenren, C., Shang, J. and Pan, J. (2016). Marginal conceptual predictive statistic for mixed model selection. Open Journal of Statistics, 6, 239-253.

Wenren, C. and Shang, J. (2016). Conditional conceptual predictive statistic for mixed model selection. Journal of Applied Statistics, 43 (4), 585-603.

Luo, J. and Shang, J. (2016). Exploratory data analysis on the unemployment rates in USA. Advances and Applications in Statistics, 48 (4), 303-316.