📚 Accepted/Published papers:
[9] Jiangzhou Wang, Meier Wu, Yang Liu, Binghui Liu and Jianhua Guo. (2025). Joint community detection in random effects stochastic block models via the split-likelihood method, Journal of Computational and Graphical Statistics, 1-57.
[8] Jiangzhou Wang, Binghui Liu, Jianhua Guo and Bing-yi Jing. (2025). Understanding asymptotic consistency and its unique advantages in large sample statistical inference, Journal of Multivariate Analysis, 1-17.
[7] Peng Luo, Jiangzhou Wang, Yilong Wu and Wei Zhang. (2025). Inference of treatment benefit rate and treatment harm rate with missing endpoint and covariate. Statistics and Its Interface, 1-27. (alphabetical order).
[6] Canhui Li, Jiangzhou Wang and Pengfei Wang. (2024). Large-scale dependent multiple testing via higher-order hidden Markov models. Journal of Biopharmaceutical Statistics, 1-13. (alphabetical order).
[5] Jiangzhou Wang, Pengfei Wang. (2024). Large‑scale dependent multiple testing via hidden semi‑Markov models, Computational Statistics, 39, 1093–1126.
[4] Jiangzhou Wang, Pengfei Wang, Tingting Cui and Wensheng Zhu. (2023). Covariate-modulated large-scale multiple testing under dependence, Computational Statistics and Data Analysis, 180, 107664.
[3] Jiangzhou Wang, Jingfei Zhang, Binghui Liu, Ji Zhu and Jianhua Guo. (2023). Fast network community detection with profile-pseudo likelihood methods, Journal of the American Statistical Association, 118(542), 1359–1372.
[2] Jiangzhou Wang, Binghui Liu and Jianhua Guo. (2021). Efficient split likelihood-based method for community detection of large-scale networks, Stat, 10(1), e349.
[1] Jiangzhou Wang, Jianhua Guo and Binghui Liu. (2021). A fast algorithm for integrative community detection of multi-layer networks, Stat, 10(1), e348.
📝 Submitted papers:
[1] Bing-yi Jing, Ting Li, Jiangzhou Wang and Ya Wang. Two-way node popularity model for directed and bipartite networks. Journal of Machine Learning Research. R&R, (alphabetical order).