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Journal of the American Statistical Association Volume 116, 2021 - Issue 533
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Applications and Case Studies

Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification

Lucy Xiaa Department of ISOM, School of Business and Management, Hong Kong University of Science and Technology, Kowloon, Hong KongView further author information
,
Richard Zhaob Department of Computer Science and Software Engineering, The Behrend College, The Pennsylvania State University, Erie, PAORCID Iconhttps://orcid.org/0000-0001-8257-4291View further author information
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Yanhui Wuc Faculty of Business and Economics, University of Hong Kong, Pokfulam, Hong Kong;d Department of Economics and Finance, University of Southern California, Los Angeles, CACorrespondence[email protected]
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&
Xin Tonge Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CACorrespondence[email protected]
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Pages 68-81 | Received 07 Mar 2019, Accepted 04 Mar 2020, Published online: 17 Apr 2020

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Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong. (2024) Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data. Journal of the American Statistical Association 119:545, pages 39-51.
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Articles from other publishers (2)

Henry W. J. Reeve, Timothy I. Cannings & Richard J. Samworth. (2023) Optimal subgroup selection. The Annals of Statistics 51:6.
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Lin He, Jiaqi Guo & Jiaxin Lin. (2022) Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification. Wireless Communications and Mobile Computing 2022, pages 1-10.
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