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Journal of Applied Statistics Volume 50, 2023 - Issue 14: Modern Streaming Data Analytics
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Editorial

Editorial to the special issue: modern streaming data analytics

Yajun Meia H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1015-990XView further author information
(Principal Guest Editor) ORCID Icon,
Jay Bartroffb Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USAView further author information
(Co-Editor) ,
Jie Chenc Division of Biostatistics and Data Science, Department of Population Health Science, Augusta University, Augusta, GA, USAView further author information
(Co-Editor) ,
Georgios Fellourisd Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USAView further author information
(Co-Editor) &
Ruizhi Zhange Department of Statistics, University of Georgia, Athens, GA, USAView further author information
(Co-Editor)
Pages 2857-2861 | Published online: 05 Oct 2023

References

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  • H. Tian, R.Z. Cohen, C. Zhang, and Y. Mei, Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation, J. Appl. Stat. 50 (2023), pp. 2951–2969.
  • K.W. Tsang, F. Tsung, and Z. Xu, Knockoff procedure for false discovery rate control in high-dimensional data streams, J. Appl. Stat. 50 (2023), pp. 2970–2983.
  • Y. Zhan, R. Zhang, Y. Zhou, V. Stoerger, J. Hiller, T. Awada, and Y. Ge, Rapid online plant leaf area change detection with high-throughput plant image data, J. Appl. Stat. 50 (2023), pp. 2984–2998.
  • Y. Zhao, X. Huo, and Y. Mei, Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition, J. Appl. Stat. 50 (2023), pp. 2999–3029.

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