Publications
Representative Publications
D. Shi, T. Wang, Z. Ying. Simultaneous identification of sparse structures and communities in heterogeneous graphical models. Journal of Machine Learning Research, 27(148): 1–63, 2026.
D. Cirkovic, T. Wang and D.B.H. Cline. Emergence of multivariate extremes in multilayer inhomogeneous random graphs. Stochastic Processes and their Applications, 190: 104762, 2025.
D. Cirkovic, T. Wang and X. Zhang. Likelihood-based inference for random networks with changepoints. IEEE Transactions on Network Science and Engineering, 13: 344–359, 2025.
D. Cirkovic, T. Wang. Modeling random networks with heterogeneous reciprocity. Journal of Machine Learning Research, 25(10): 1–40, 2024.
T. Wang and S.I. Resnick. Degree growth rates and index estimation in a directed preferential attachment model. Stochastic Processes and their Applications, 130(2): 878–906, 2020.
Books
- T. Wang, Y. Sun, Y. Xia, C. Guo, Y. Fu, and X. Huang. Data Governance and Model Optimization: Methodological Practices Based on Agile Statistics [in Chinese]. Publishing House of Electronics Industry, 2026. ISBN 978-7-121-53268-9.
Journal Articles
2026
D. Shi, T. Wang, Z. Ying. Simultaneous identification of sparse structures and communities in heterogeneous graphical models. Journal of Machine Learning Research, 27(148): 1–63, 2026.
Q. Hui, S.I. Resnick, T. Wang. Classification of extremal dependence in financial markets via bootstrap inference. Journal of Time Series Analysis, 2026. DOI: 10.1111/jtsa.70078.
Q. Hui and T. Wang. Extremal dependence networks for mitigating systemic risk via maximum independent sets. Journal of Complex Networks, 14(4): cnag005, 2026.
Z. Han and T. Wang. Measuring interlayer dependence of large degrees in multilayer inhomogeneous random graphs. Physica A: Statistical Mechanics and its Applications, 682: 131173, 2026.
2025
Z. Han, T. Wang and Z. Ying. Multilayer network regression with eigenvector centrality and community structure. Statistica Sinica, 2025. DOI: 10.5705/ss.202025.0101.
D. Cirkovic, T. Wang and D.B.H. Cline. Emergence of multivariate extremes in multilayer inhomogeneous random graphs. Stochastic Processes and their Applications, 190: 104762, 2025.
D. Cirkovic, T. Wang and X. Zhang. Likelihood-based inference for random networks with changepoints. IEEE Transactions on Network Science and Engineering, 13: 344–359, 2025.
T. Wang and S.I. Resnick. Distinguishing forms of asymptotic dependence in heavy tailed data. Statistica Sinica, 2025. DOI: 10.5705/ss.202024.0196.
2024
Z. Li, Y. Hou, T. Wang. Recent advances on mechanisms of network generation: Community, exchangeability, and scale‐free properties. Wiley Interdisciplinary Reviews: Computational Statistics, 16(2): e1651, 2024.
D. Cirkovic, T. Wang. Modeling random networks with heterogeneous reciprocity. Journal of Machine Learning Research, 25(10): 1–40, 2024.
T. Wang and S.I. Resnick. Random networks with heterogeneous reciprocity levels. Extremes, 27(1): 123–161, 2024.
2023
D. Cirkovic, T. Wang and S.I. Resnick. Preferential attachment with reciprocity: Properties and estimation. Journal of Complex Networks, 11(5): cnad031, 2023.
Y. Yuan, T. Wang, P. Zhang and J. Yan. Generating general preferential attachment networks with R package
wdnet. Journal of Data Science, 21(3): 538–556, 2023.T. Wang and S.I. Resnick. Common growth patterns in regional communication networks: a point process approach. Journal of Data Science, 21(3): 446–469, 2023.
T. Wang and S.I. Resnick. Poisson edge growth and preferential attachment networks. Methodology and Computing of Applied Probability, 25(1): 8, 2023.
2022
T. Wang, J. Yan, Y. Yuan and P. Zhang. Generating directed networks with predetermined assortativity measures. Statistics and Computing, 32(5): 91, 2022.
J. Wang, Y. Hou, X. Li and T. Wang. EVIboost for the estimation of extreme value index under heterogeneous extremes. Journal of Data Science, 21(4): 638–657, 2022.
T. Wang and S.I. Resnick. Measuring reciprocity in a directed preferential attachment network. Advances in Applied Probability, 54(3): 718–742, 2022.
T. Wang and S.I. Resnick. Asymptotic dependence of in- and out-degrees in a preferential attachment model with reciprocity. Extremes, 25(3): 417–450, 2022.
T. Wang and P. Zhang. Hybrid random networks mixing preferential attachment with uniform attachment mechanisms. Annals of the Institute of Statistical Mathematics, 74(5): 957–986, 2022.
C. Tang, T. Wang and P. Zhang. Functional data analysis: An application to COVID-19 data in the United States. Quantitative Biology, 10(2): 172–187, 2022.
B. Das, T. Wang and G. Dai. Asymptotic behavior of common connections in sparse random networks. Methodology and Computing in Applied Probability, 24: 2071–2092, 2022.
P. Zhang, T. Wang and J. Yan. PageRank centrality and algorithms for weighted, directed networks. Physica A: Statistical Mechanics and its Applications, 586: 126438, 2022.
P. Zhang, T. Wang and S.X. Xie. Meta-analysis of several epidemic characteristics of COVID-19. Journal of Data Science, 18(3): 536–549, 2022.
2021
- T. Wang and J. Yan. Discussion of “On Studying Extreme Values and Systematic Risks with Nonlinear Time Series Models and Tail Dependence Measures”. Statistical Theory and Related Fields, 5(1): 38–40, 2021.
2020
T. Wang and S.I. Resnick. Degree growth rates and index estimation in a directed preferential attachment model. Stochastic Processes and their Applications, 130(2): 878–906, 2020.
H. Drees, A. Janßen, S.I. Resnick and T. Wang. On a minimum distance procedure for threshold selection in tail analysis. SIAM Journal on Mathematics of Data Science, 2(1): 75–102, 2020.
P. Wan, T. Wang, R.A. Davis, and S.I. Resnick. Are extreme value estimation methods useful for network data? Extremes, 23(1): 171–195, 2020.
2019
- T. Wang and S.I. Resnick. Consistency of Hill estimators in a linear preferential attachment model. Extremes, 22(1): 1–28, 2019.
2018
- T. Wang and S.I. Resnick. Multivariate regular variation of discrete mass functions with applications to preferential attachment networks. Methodology and Computing in Applied Probability, 20: 1029–1042, 2018.
2017
P. Wan, T. Wang, R.A. Davis and S.I. Resnick. Fitting the linear preferential attachment model. Electronic Journal of Statistics, 11(2): 3738–3780, 2017.
T. Wang and S.I. Resnick. Asymptotic normality of in- and out-degree counts in a preferential attachment model. Stochastic Models, 33(2): 229–255, 2017.
Y. Fan, P. Griffin, R.A. Maller, A. Szimayer and T. Wang. The effects of largest claims and excess of loss reinsurance on a company’s ruin time and valuation. Risks, 5(1): 3, 2017.
Preprints and Working Papers
Under Revision
D. Shi, H. Zhang, T. Wang, J. Wang. A multilayer probit network model for community detection with dependent edges and layers. Minor revision submitted to Journal of the American Statistical Association, 2026.
T. Wang and S.I. Resnick. Bootstrap methods for testing asymptotic dependence in multivariate heavy-tailed data. Revision submitted to Extremes, 2026.
Submitted
D. Cirkovic and T. Wang. Efficient topic model estimation under heavy-tailed document lengths. arXiv:2607.24641. Submitted to Journal of Machine Learning Research, 2026.
Q. Hui and T. Wang. Hub neighbor-degree diagnostics for sparse random graphs. arXiv:2607.26624. Submitted to IEEE Transactions on Network Science and Engineering, 2026.
D. Cirkovic, T. Wang, D.B.H. Cline. On tail inference in scale-free inhomogeneous random graphs. arXiv:2410.22703. Submitted to Bernoulli, 2026.
F. Fang, S. Bai, T. Wang. Structural causal models for extremes: an approach based on exponent measures. arXiv:2508.00223. Submitted to Annals of Applied Probability, 2026.