Wei Biao Wu

5.7k total citations
158 papers, 3.2k citations indexed

About

Wei Biao Wu is a scholar working on Statistics and Probability, Finance and Artificial Intelligence. According to data from OpenAlex, Wei Biao Wu has authored 158 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Statistics and Probability, 60 papers in Finance and 33 papers in Artificial Intelligence. Recurrent topics in Wei Biao Wu's work include Financial Risk and Volatility Modeling (56 papers), Statistical Methods and Inference (48 papers) and Stochastic processes and financial applications (27 papers). Wei Biao Wu is often cited by papers focused on Financial Risk and Volatility Modeling (56 papers), Statistical Methods and Inference (48 papers) and Stochastic processes and financial applications (27 papers). Wei Biao Wu collaborates with scholars based in United States, China and United Kingdom. Wei Biao Wu's co-authors include Xiaofeng Shao, Zhibiao Zhao, Zhou Zhou, Xiao Han, Danna Zhang, Michael Woodroofe, Weidong Liu, Jan Mielniczuk, Wanli Min and Magda Peligrad and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.

In The Last Decade

Wei Biao Wu

146 papers receiving 3.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Wei Biao Wu United States 30 1.6k 1.3k 712 516 331 158 3.2k
Paul Doukhan France 21 1.7k 1.1× 1.9k 1.5× 868 1.2× 800 1.6× 285 0.9× 63 3.6k
Keith Knight Canada 21 2.1k 1.3× 675 0.5× 495 0.7× 828 1.6× 322 1.0× 37 4.2k
Song Xi Chen China 35 2.6k 1.6× 816 0.6× 429 0.6× 1.0k 1.9× 209 0.6× 121 4.3k
Tailen Hsing United States 22 1.1k 0.7× 1.0k 0.8× 417 0.6× 511 1.0× 151 0.5× 61 2.3k
Piotr Kokoszka United States 35 2.4k 1.5× 2.6k 2.0× 2.1k 2.9× 702 1.4× 761 2.3× 163 5.4k
Denis Bosq France 14 1.4k 0.9× 775 0.6× 411 0.6× 571 1.1× 160 0.5× 63 2.4k
Qiwei Yao United Kingdom 29 1.6k 1.0× 1.4k 1.1× 1.2k 1.7× 561 1.1× 627 1.9× 98 3.7k
Vladimir Spokoiny Germany 25 1.3k 0.8× 471 0.4× 314 0.4× 679 1.3× 178 0.5× 107 2.7k
Peter Hall Australia 36 3.1k 1.9× 397 0.3× 565 0.8× 1.2k 2.2× 211 0.6× 108 4.6k
Robert Serfling United States 27 4.3k 2.7× 888 0.7× 505 0.7× 1.2k 2.3× 215 0.6× 76 6.5k

Countries citing papers authored by Wei Biao Wu

Since Specialization
Citations

This map shows the geographic impact of Wei Biao Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wei Biao Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Biao Wu more than expected).

Fields of papers citing papers by Wei Biao Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei Biao Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wei Biao Wu. The network helps show where Wei Biao Wu may publish in the future.

Co-authorship network of co-authors of Wei Biao Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Biao Wu. A scholar is included among the top collaborators of Wei Biao Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wei Biao Wu. Wei Biao Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wu, Yanhong, Wei Biao Wu, & Dong‐Yun Kim. (2025). Spectral Norm of Exponentially Weighted Moving Sample Covariance Matrix and Its Application to Sequential Sparse Signal Detection. Journal of Statistical Theory and Practice. 19(2).
2.
Shao, Ling, Qian Wang, Liyu Shi, et al.. (2024). Genome-wide identification and expression profile of HIR gene family members in Oryza sativa L. Frontiers in Plant Science. 15. 1492026–1492026.
3.
Wu, Yanhong, Wei Biao Wu, & Dong‐Yun Kim. (2024). A comparative study on sequential detection of random mean change in multivariate normal data stream. Sequential Analysis. 43(4). 477–496. 1 indexed citations
4.
Liu, Hanqing, Jiacheng Yang, Chia‐Hao Chang, et al.. (2023). AOG-LSTM: An adaptive attention neural network for visual storytelling. Neurocomputing. 552. 126486–126486. 7 indexed citations
5.
Richter, Stefan, Ning Wang, & Wei Biao Wu. (2023). Testing for parameter change epochs in GARCH time series. Econometrics Journal. 26(3). 467–491. 4 indexed citations
6.
Wu, Yanhong & Wei Biao Wu. (2022). Sequential common rate decrease detection, isolation, and estimation in multiple Poisson processes. Journal of Statistical Computation and Simulation. 92(18). 3817–3835. 1 indexed citations
7.
Wu, Yanhong & Wei Biao Wu. (2022). Sequential common change detection, isolation, and estimation in multiple poisson processes. Sequential Analysis. 41(2). 176–197.
8.
Wu, Yanhong & Wei Biao Wu. (2021). Sequential detection of common transient signals in high dimensional data stream. Naval Research Logistics (NRL). 69(4). 640–653. 5 indexed citations
9.
Wu, Wei Biao, et al.. (2020). Sharp connections between Berry-Esseen characteristics and Edgeworth expansions for stationary processes. Transactions of the American Mathematical Society. 374(6). 4129–4183. 5 indexed citations
10.
Wu, Wei Biao, et al.. (2020). Asymptotic theory for QMLE for the real‐time GARCH(1,1) model. Journal of Time Series Analysis. 42(5-6). 752–776. 5 indexed citations
11.
Wu, Wei Biao, et al.. (2018). Concentration inequalities for empirical processes of linear time series. Journal of Machine Learning Research. 18(231). 1–46. 6 indexed citations
12.
Wu, Wei Biao, et al.. (2014). Mathematical model for flood routing based on cellular automaton. SHILAP Revista de lepidopterología. 14 indexed citations
13.
Zhang, Ting, et al.. (2013). Block sampling under strong dependence. Stochastic Processes and their Applications. 123(6). 2323–2339. 11 indexed citations
14.
Beutner, Eric, Wei Biao Wu, & Henryk Zähle. (2011). Asymptotics for statistical functionals of long-memory sequences. Stochastic Processes and their Applications. 122(3). 910–929. 10 indexed citations
15.
Zhou, Zhou & Wei Biao Wu. (2010). On linear models with long memory and heavy-tailed errors. Journal of Multivariate Analysis. 102(2). 349–362. 8 indexed citations
16.
Wu, Wei Biao, et al.. (2010). Kernel estimation for time series: An asymptotic theory. Stochastic Processes and their Applications. 120(12). 2412–2431. 20 indexed citations
17.
Zhao, Zhibiao & Wei Biao Wu. (2006). Asymptotic theory for curve-crossing analysis. Stochastic Processes and their Applications. 117(7). 862–877. 3 indexed citations
18.
Peligrad, Magda, Sergey Utev, & Wei Biao Wu. (2006). A maximal 𝕃_{𝕡}-inequality for stationary sequences and its applications. Proceedings of the American Mathematical Society. 135(2). 541–550. 33 indexed citations
19.
Wu, Wei Biao & Wanli Min. (2005). On linear processes with dependent innovations. Stochastic Processes and their Applications. 115(6). 939–958. 70 indexed citations
20.
Fessler, Jeffrey A., Hakan Erdoğan, & Wei Biao Wu. (2000). Exact distribution of edge-preserving MAP estimators for linear signal models with Gaussian measurement noise. IEEE Transactions on Image Processing. 9(6). 1049–1055. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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