Paper Publications

• [1]High-dimensional statistical inference via DATE, Communications in Statistics- Theory and Methods, 2021.
• [2]Zheng, Z.*, Lv, J. and Lin, W. (2021). Nonsparse learning with latent variables. Operations Research 69(1), 346-359.
• [3]Dong, R., Li, D. and Zheng, Z.* (2021). Parallel integrative learning for large-scale multi-response regression with incomplete outcomes. Computational Statistics & Data Analysis 160, 107243.
• [4]Zhou, J., Zheng, Z.*, Zhou, H. and Dong, R. (2021). Innovated scalable efficient inference for ultra-large graphical models. Statistics & Probability Letters 173, 109085.
• [5]Zheng, Z., Li, Y., Wu, J.* and Wang, Y. (2020). Sequential scaled sparse factor regression. Journal of Business & Economic Statistics, DOI: 10.1080/07350015.2020.1844212.
• [6]Zheng, Z., Zhang, J.*, Li, Y. and Wu, Y. (2020). Partitioned approach for high-dimensional confidence intervals with large split sizes. Statistica Sinica, DOI: 10.5705/ss.202018.0379.
• [7]Zheng, Z., Shi, H., Li, Y.* and Yuan, H. (2020). Uniform joint screening for ultra-high dimensional graphical models. Journal of Multivariate Analysis 179, 104645.
• [8]Wu, J., Zheng, Z.*, Li, Y. and Zhang, Y. (2020). Scalable interpretable learning for multi-response error-in-variables regression. Journal of Multivariate Analysis 179, 104644.
• [9]Zheng, Z., Li, L., Zhou, J.* and Kong, Y. (2020). Innovated scalable dynamic learning for time-varying graphical models. Statistics & Probability Letters 165, 108843.
• [10]Zheng, Z.*, Bahadori, M. T., Liu, Y. and Lv, J. (2019). Scalable interpretable multi-response regression via SEED. Journal of Machine Learning Research 20, 1-34.