2023年1月至12月,统计科学中心教员共发表论文166篇。以下为部分代表性论文(按第一作者姓氏字母排序排列):
[1]Chen, Hui; Ren, Haojie; Yao, Fang; Zou, Changliang(2023). Data-driven selection of the number of change-points via error rate control. Journal of the American Statistical Association, 118(542), 1415-1428.
[2]Chen, Song Xi; Guo, Bin; Qiu, Yumou(2023). Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding. Journal of Econometrics, 235(2), 1337-1354.
[3]Chen, Ziyuan; Yang, ying; Yao, Fang(2023). Dynamic matrix recovery. Journal of the American Statistical Association, published online, https://doi.org/10.48550/arXiv.2305.10524.
[4]Cui, Yifan; Pu, Hongming; Shi, Xu; Miao, Wang; Tchetgen, Eric Tchetgen(2023). Semiparametric proximal causal inference. Journal of the American Statistical Association, published online, https://doi.org/10.1080/01621459.2023.2191817.
[5]Ding, Jian; Du, Hang(2023). Matching recovery threshold for correlated random graphs. Annals of Statistics, 51(4), 1718-1743.
[6]Ding, Jian; Gwynne, Ewain(2023). Tightness of supercritical liouville first passage percolation. Journal of the European Mathematical Society, 25(10), 3833-3911.
[7]Ding, Jian; Gwynne, Ewain(2023). Uniqueness of the critical and supercritical liouville quantum gravity metrics. Proceedings of the London Mathematical Society, 126(1), 216-333.
[8]Ding, Jian; Liu, Haoyu(2023). Shotgun assembly threshold for lattice labeling model. Probability Theory and Related Fields, 187, 423-442.
[9]Ding, Jian; Song, Jian; Sun, Rongfeng(2023). A new correlation inequality for Ising models with external fields. Probability Theory and Related Fields, 186, 477492.
[10]Ding, Jian; Wu, Yihong; Xu, Jiaming; Yang, Dana(2023). The planted matching problem: sharp threshold and infinite-order phase transition. Probability Theory and Related Fields, published online, https://doi.org/10.1007/s00440-023-01208-6.
[11]Ding, Jian; Zhuang, Zijie(2023). Long range order for random field Ising and potts models. Communications on Pure and Applied Mathematics, 77(1), 37-51.
[12]Fang, Zhuangyan; Zhu, Shengyu; Zhang, Jiji; Liu, Yue; Chen, Zhitang; He, Yangbo(2023). On low-rank directed acyclic graphs and causal structure learning. IEEE Transactions on Neural Networks and Learning Systems, published online, https://doi.org/10.1109/tnnls.2023.3273353.
[13]Gu, Zhonghui; Luo, Xiao; Chen, Jiaxiao; Deng, Minghua; Lai, Luhua(2023). Hierarchical graph transformer with contrastive learning for protein function prediction. Bioinformatics, 39(7).
[14]Li, Haoxuan; Zheng, Chunyuan; Cao, Yixiao; Geng, Zhi; Liu, Yue; Wu, Peng(2023). Trustworthy policy learning under the counterfactual no-harm criterion. International Conference on Machine Learning, https://openreview.net/pdf?id=s5v1TJklbL.
[15]Li, Kendrick Qijun; Shi, Xu; Miao, Wang; Tchetgen, Eric Tchetgen(2023). Double negative control inference in test-negative design studies of vaccine effectiveness. Journal of the American Statistical Association, published online, https://doi.org/10.1080/01621459.2023.2220935.
[16]Li, Shuo; Peng, Liuhua; Song, Xiaojun(2023). Simultaneous confidence bands for conditional value-at-risk and expected shortfall. Econometric Theory, 39(5), 1009-1043.
[17]Li, Xinyu; Miao, Wang; Lu, Fang; Zhou, Xiao-Hua(2023). Improving efficiency of inference in clinical trials with external control data. Biometrics, 79(1), 394-403.
[18]Li, Yilin; Miao, Wang; Shpitser, Ilya; Eric J. Tchetgen Tchetgen.(2023). A self-censoring model for multivariate nonignorable nonmonotone missing data. Biometrics, accepted on 10 July 2023, https://doi.org/10.1111/biom.13916.
[19]Liang, Decai; Huang, Hui; Guan, Yongtao; Yao, Fang(2023). Test of weak separability for spatially stationary functional field. Journal of the American Statistical Association, 118(543), 1606-1619.
[20]Lin, Yingqian; Tu, Yundong(2023). Transformation models with cointegrated and deterministically trending regressors. Essays in Honor of Joon Y. Park: Econometric Theory,45A,207-232.
[21]Lo, Andrew W.; Zhang, Ruixun(2023). Quantifying the impact of impact investing. Management Science, published online, https://doi.org/10.1287/mnsc.2022.01168.
[22]Luo, Shanshan; Li, Wei; He, Yangbo(2023). Causal inference with outcomes truncated by death in multiarm studies. Biometrics, 79(1), 502-513.
[23]Ma, Chenchen; Tu, Yundong(2023). Group fused lasso for large factor models with multiple structural breaks. Journal of Econometrics, 233(1), 132-154.
[24]Ma, Chenchen; Tu, Yundong(2023). Shrinkage estimation of multiple threshold factor models. Journal of Econometrics, 235(2), 1876-1892.
[25]Ma, Yingying, Guo, Sshaojun; Wang, Hansheng(2023). Sparse spatio-temporal autoregressions by profiling and bagging. Journal of Econometrics, 232(1), 132-147.
[26]Peng, Ting; Hou, Yingping; Meng, Haowei; Cao, Yong; Wang, Xiaotian; Jia, Lumeng; Chen, Qing; Zheng, Yang; Sun, Yujie; Chen, Hebing; Li, Tingting; Li, Cheng(2023). Mapping nucleolus-associated chromatin interactions using nucleolus Hi-C reveals pattern of heterochromatin interactions. Nature Communications, 14(1), https://doi.org/10.1038/s41467-023-36021-1.
[27]Qiu, Yumou; Sun, Jiarui; Zhou, Xiao-Hua(2023). Unveiling the unobservable: causal inference on multiple derived outcomes. Journal of the American Statistical Association, published online, https://doi.org/10.1080/01621459.2023.2252135.
[28]Tu, Yundong; Xie, Xinling(2023). Penetrating sporadic return predictability. Journal of Econometrics, 237, 105509.
[29]Wu, Shuyuan; Huang, Danyang; Wang, Hansheng(2023). Quasi-newton updating for large-scale distributed learning. Journal of the Royal Statistical Society Series B-statistical Methodology, 85(4), 1326-1354.
[30]Xia, Yuchao; Jin, Zijie; Zhang, Chengsheng; Ouyang, Linkun; Dong, Yuhao; Li, Juan; Guo, Lvze; Jing, Biyang; Shi, Yang; Miao, Susheng; Xi, Ruibin(2023). TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing. Nature Communications, 5935.
[31]Xu, Lihu; Yao Fang; Yao Qiuran; Zhang Huiming(2023). Non-asymptotic guarantees for robust statistical learning under infinite variance assumption. Journal of Machine Learning Research, 24(92), 1-46.
[32]Xue, Kaijie; Yang, Jin; Yao, Fang(2023). Optimal linear discriminant analysis for high-dimensional functional data. Journal of the American Statistical Association, published online, https://doi.org/10.1080/01621459.2022.2164288.
[33]Yang, Ying; Yao, Fang(2023). Online estimation for functional data. Journal of the American Statistical Association, 118(543), 1630-1644.
[34]Yang, Ying; Yao, Fang; Zhao, Peng(2023). Online smooth backfitting for generalized additive models. Journal of the American Statistical Association, published online, https://doi.org/10.1080/01621459.2023.2182213.
[35]Ye, Haishan; Lin, Dachao; Chang, Xiangyu; Zhang, Zhihua(2023). Towards explicit superlinear convergence rate for SR1. Mathematical Programming, 199(1-2), 1273-1303.
[36]Ying, Andrew; Miao, Wang; Shi, Xu; Tchetgen, Eric J. Tchetgen(2023). Proximal causal inference for complex longitudinal studies. Journal of the Royal Statistical Society Series B-Statistical Methodology, 85(3), 684-704.
[37]Zhou, Hang; Yao, Fang; Zhang, Huiming(2023). Functional linear regression for discretely observed data: from ideal to reality. Biometrika, 110(2).
[38]Zhu, Wenhao; Li, Lujun; Yang, Jingping; Xie, Jiehua; Sun, Liulei(2023). Asymptotic subadditivity/superadditivity of value-at-risk under tail dependence. Mathematical Finance, 33(4), 1314-1369.