近期部分论文:
[1] He, S., Li, Z.* and Liu, X. (2023). An improved GEV boosting method for imbalanced data classification with application to short-term rainfall prediction. Journal of Hydrology, 617, 128882. (JCR, Q1,中科院1区)
[2] Luo, Y., Wei, Y., Li, Z.* and Jing, B.-Y.* (2023). Incorporating Relative Error Criterion to Conformal Prediction for Positive Data. Communications in Mathematics and Statistics, DOI: https://doi.org/10.1007/s40304-023-00360-8.
[3] Li, Z.*, Xu, J., Zhao, N. and Zhou, W. (2023). Penalized jackknife empirical likelihood in high dimensions. Statistica Sinica, 33, 1219-1232.
[4] Liu, Q. and Li, Z.* (2023). Distributed estimation via empirical likelihood. The Canadian Journal of Statistics, 51, 375-399.
[5] Wei, Y., Li, Z.* and Dai, Y. (2022). Unified smoothed jackknife empirical likelihood tests for comparing income inequality indices. Statistical Papers, 63, 1415-1475.
[6] Tao, Z. and Li, Z.* (2022). Adaptive singular value shrinkage estimate for low rank tensor denoising. Random Matrices: Theory and Applications,11 (04), 2250038.
[7] Du, Y., Li, Z.* and Chen, X. (2022). Efficient empirical Bayes estimates for risk parameters of Pareto distributions. Communications in Statistics-Theory and Methods. 51(6): 1674-1692.
[8] Ji, Z., Wei, Y. and Li, Z.* (2020). SURE estimates for high dimensional classification. Statistical Analysis and Data Mining. 13 (5), 423-436.
[9] Jing, B.-Y., Li, Z.*, Pan, G. and Zhou, W. (2016). On SURE-type double shrinkage estimation. Journal of the American Statistical Association. 111 (516), 1696-1704. (统计学Top期刊)
[10]Li, Z.*, Xu, J. and Zhou, W. (2016). On nonsmooth estimating functions via jackknife empirical likelihood. Scandinavian Journal of Statistics. 43, 49-69.