1. Zheng, Zemin; Lv, Jinchi; Lin, Wei(2021). Nonsparse learning with latent variables. Operations Research , 69(1), 346-359.
2. 吴煌坚, 林伟, 孔磊, 唐晓, 王威, 王自发, 陈松蹊 (2021). 一种基于集合最优插值的排放源快速反演方法. 气候与环境研究, 26(2), 191-201.
3. Wu, H., Zheng, X., Zhu, J., Lin, W., Zheng, H., Chen, X., ... & Chen, S. X. (2020). Improving PM2. 5 Forecasts in China Using an Initial Error Transport Model. Environmental Science & Technology , 54 (17), 10493-10501.
4. 张澍一, 陈松蹊, 郭斌, 王恒放, & 林伟. (2020). 气象调整下的区域空气质量评估. 中国科学 : 数学 , 50 (4), 527-558.
5. Cao, Y., Lin, W. and Li, H. (2019). Large covariance estimation for compositional data via composition-adjusted thresholding. Journal of the American Statistical Association, 114(526), 759–772
6. Zhang, J. and Lin, W. (2019). Scalable estimation and regularization for the logistic normal multinomial model. Biometrics, 75(4), 1098–1108.
7. Uematsu, Y., Fan, Y., Chen, K., Lv, J., & Lin, W. (2019). SOFAR: Large-scale association network learning. IEEE transactions on information theory , 65 (8), 4924-4939.
8.Cao, Y., Lin, W. and Li, H. (2018). Two-sample tests of high-dimensional means for compositional data. Biometrika, 105, 115-132.
9. Cao, Y., Lin, W. and Li, H. (2018+). Large covariance estimation for compositional data via composition-adjusted thresholding. Journal of the American Statistical Association, to appear.
