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|a 10.1080/02664763.2021.1911967
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|a eng
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| 100 |
1 |
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|a Xiaoyue, Xie
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A distributed multiple sample testing for massive data
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 17.09.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Informa UK Limited, trading as Taylor & Francis Group.
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|a When the data are stored in a distributed manner, direct application of traditional hypothesis testing procedures is often prohibitive due to communication costs and privacy concerns. This paper mainly develops and investigates a distributed two-node Kolmogorov-Smirnov hypothesis testing scheme, implemented by the divide-and-conquer strategy. In addition, this paper also provides a distributed fraud detection and a distribution-based classification for multi-node machines based on the proposed hypothesis testing scheme. The distributed fraud detection is to detect which node stores fraud data in multi-node machines and the distribution-based classification is to determine whether the multi-node distributions differ and classify different distributions. These methods can improve the accuracy of statistical inference in a distributed storage architecture. Furthermore, this paper verifies the feasibility of the proposed methods by simulation and real example studies
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|a Journal Article
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|a Distributed scheme
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|a classification
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|a fraud detection
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|a hypothesis testing
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| 700 |
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|a Shi, Jian
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|a Song, Kai
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