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Journal of Applied Statistics Volume 50, 2023 - Issue 3: Statistical Approaches for Big Data and Machine Learning
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Modeling multivariate cyber risks: deep learning dating extreme value theory

Mingyue Zhang Wua School of Mathematics and Statistics and RIMS, Jiangsu Provincial Key Laboratory of Educational Big Data Science and Engineering, Jiangsu Normal University, ChinaView further author information
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Jinzhu Luob School of Information Technology, Illinois State University, Normal, IL, USAView further author information
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Xing Fangb School of Information Technology, Illinois State University, Normal, IL, USAView further author information
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Maochao Xuc Department of Mathematics, Illinois State University, Normal, IL, USACorrespondence[email protected]
View further author information
&
Peng Zhaoa School of Mathematics and Statistics and RIMS, Jiangsu Provincial Key Laboratory of Educational Big Data Science and Engineering, Jiangsu Normal University, ChinaView further author information
Pages 610-630 | Received 28 Sep 2020, Accepted 24 May 2021, Published online: 04 Jun 2021

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Read on this site (1)

Yichuan Zhao, Chi-Hua Chen, Feng Feng & Dragan Pamucar. (2023) Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning. Journal of Applied Statistics 50:3, pages 451-455.
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Articles from other publishers (3)

Rong He, Zhuo Jin & Johnny Siu-Hang Li. (2024) Modeling and management of cyber risk: a cross-disciplinary review. Annals of Actuarial Science, pages 1-40.
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Daniel Zängerle & Dirk Schiereck. (2022) Modelling and predicting enterprise-level cyber risks in the context of sparse data availability. The Geneva Papers on Risk and Insurance - Issues and Practice 48:2, pages 434-462.
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Yuying Li & Rogemar Mamon. (2023) The Price Tag of Cyber Risk: A Signal-Processing Approach. IEEE Access 11, pages 44294-44318.
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