Publications
Gao, Y., Zhang, Z., Cai Z., Zhu, X., Zou, T., Wang, H. (2024) “Penalized Sparse Covariance Regression with High Dimensional Covariates”, Journal of Business & Economic Statistics, accepted.
Zhang, Y., Pan, R., Zhu, X., Fang, K., Wang, H. (2024) “A Latent Space Model for Weighted Keyword Co-occurrence Networks with Applications in Knowledge Discovery in Statistics”, Journal of Computational and Graphical Statistics, accepted.
Ren, Y., Li, Z., Zhu, X., Gao., Y., Wang, H. (2023) “Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks”, Journal of Econometrics, accepted. (Joint work with my students Yimeng Ren and Zhe Li)
Fang, G., Xu, G., Xu, H., Zhu, X., Guan, Y. (2023) “Group Network Hawkes Process”, Journal of the American Statistical Association, accepted
Zhu, X., Xu, G., Fan, J. (2023) “Simultaneous Estimation and Group Identification for Network Vector Autoregressive Model with Heterogeneous Nodes”, Journal of Econometrics, accepted
Wu, S., Li, Z., Zhu, X. (2023) “A Distributed Community Detection Algorithm for Large Scale Networks Under Stochastic Block Models”, Computational Statistics & Data Analysis, 107794. (Joint work with my students Shihao Wu and Zhe Li)
Pan, R., Zhu, Y., Guo, B., Zhu, X., Wang, H. (2023) “A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint”, IEEE Transactions on Knowledge and Data Engineering.
Li, X., Zhu, X., Wang, H. (2023) “Distributed Logistic Regression for Massive Data with Rare Events”, Statistica Sinica, accepted
Zhang, J., Cai, B., Zhu, X., Wang, H., Xu, G., Guan, Y. (2023) “Learning Human Activity Patterns using Clustered Point Processes with Active and Inactive States”, Journal of Business & Economic Statistics, 41(2), 388-398.
Chen, E., Fan, J., Zhu, X. (2023) “Community Network Auto-Regression for High-Dimensional Time Series”, Journal of Econometrics, 235(2), 1239-1256.
Ren, Y., Zhu, X., Lu, X., Hu, G. (2022) “Graphical Assistant Grouped Network Autoregression Model: a Bayesian Nonparametric Recourse”, Journal of Business & Economic Statistics, online. (Joint work with my student Yimeng Ren)
Gao, Y., Zhu, X., Qi, H., Li, G., Zhang, R., Wang, H. (2022) “An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models”, Journal of Computational and Graphical Statistics, online.
Pan, R., Chang, X., Zhu, X., Wang, H. (2022) “Link prediction via latent space logistic regression model”, Statistics and Its Interface, 15, 267-282.
Zeng, Q., Zhu, Y., Zhu, X., Wang, F., Zhao, W., Sun, S., Su, M., Wang, H. (2022) “Improved Naive Bayes with Mislabeled Data”, Statistics and Its Interface, accepted
Qi, H., Zhu, X., Wang, H. (2022) “A Random Projection Method for Large-Scale Community Detection”, Statistics and Its Interface, accepted
Guo, B., Wang, L., Pan, R., Zhu, X. (2022) “A grouped spatial-temporal model for PM2.5 data and its applications on outlier detection”, Communications in Statistics - Simulation and Computation, online.
Zhu, X., Cai, Z., Ma, Y. (2022) “Network functional varying coefficient model”, Journal of the American Statistical Association, 117(540), 2074-2085.
Zhu, X., Pan, R., Wu, S., Wang, H. (2022) “Feature Screening for Massive Data Analysis by Subsampling”, Journal of Business & Economic Statistics, 40(4), 1892-1903.
Wu, S., Zhu, X., Wang, H. (2021) “Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources”, Statistica Sinica, accepted
Zhu, X., Li, F., Wang, H. (2021) “Least-Square Approximation for a Distributed System”, Journal of Computational and Graphical Statistics, 30(4), 1004-1018.
王菲菲, 朱雪宁, 潘蕊 (2021), “广义网络向量自回归”, 中国科学：数学, 2021, 8:1253-1266.
Huang, D., Zhu, X., Li, R., and Wang, H. (2021), “Feature screening for network autoregression model”, Statistica Sinica, 31, 1239-1259. [Supplement]
Zhu, X., Pan, R., Zhang, Y., Chen, Y., Mi, W., Wang, H. (2021) “Information diffusion with network structures”, Statistics and Its Interface, 14, 115-129.
Huang, D., Zhu, X., Luo, W., Yin, H., Hong, J., Chen, Y., Zhou, J., Wang, H. (2021) “On Identification of High Risk Carriers of COVID-19 Using Masked Mobile Device Data”, Journal of Data Science, 18(5), 849-859.
Zhu, X. (2020) “Nonconcave penalized estimation in sparse vector autoregression model,” Electronic Journal of Statistics, 14, 1413-1448.
Zhu, X., and Pan, R. (2020), “Grouped network vector autoregression,” Statistica Sinica, 30, 1437-1462. [Code]
Zhu, X., Huang, D., Pan, R., and Wang, H. (2020), “Multivariate spatial autoregression for large scale social network,” Journal of Econometrics, 215, 591-606.[Code]
Xu, K., Sun, L., Liu, J., Zhu, X., and Wang, H. (2020) “A spatial autoregression model with time-varying coefficients,” Statistics and Its Interface, 13, 261-270.
Huang, D., Wang, F., Zhu, X., and Wang, H. (2020), “Two-mode network autoregressive model for large-scale networks,” Journal of Econometrics, 216, 203-219.
Zhang, X., Pan, R., Guan, G., Zhu, X., and Wang, H. (2020), “Network logistic regression model”, Statistica Sinica, 30, 673-693.
Zhu, X., Chang, X., Li, R., and Wang, H. (2019), “Portal nodes screening for large scale social networks,” Journal of Econometrics, 209, 145-157.
Zhu, X., Wang, W., Wang, H., and Hardle, W. (2019), “Network quantile autoregression,” Journal of Econometrics, 212, 345-358.
Pan, R., Guan, R., and Zhu, X. (2018), “A latent moving average model for network regression,” Statistics and Its Interface, 11, 641-648.
Cai, W., Guan, G., Pan, R., Zhu, X., and Wang, H. (2018), “Network linear discriminant analysis,” Computational Statistics and Data Analysis, 117, 32-44.
Zhu, X., Pan, R., Li, G., Liu, Y., and Wang, H. (2017), “Network vector autoregression”, Annals of Statistics, 45, 1096-1123. [Supplement][Code]
Zhu, X., Huang, D., Pan, R., and Wang, H. (2016), “An EM algorithm for click fraud detection,” Statistics and Its Interface, 9, 389-394.
Books
朱雪宁 等, (2021). 统计分析（以R语言为工具）. [Code & Data]
朱雪宁 等, (2018). R语言：从数据思维到数据实战. 中国人民大学出版社 (ISBN: 978-7-300-26311-3). [Code & Data]
- Lecture 1. 初识R语言 [Slide]