A distributed multiple sample testing for massive data.

Content Provider	Europe PMC
Author	Xiaoyue, Xie Shi, Jian Song, Kai
Copyright Year	2021
Abstract	ABSTRACT 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.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10128098&blobtype=pdf
Page Count	19
ISSN	02664763
Volume Number	50
DOI	10.1080/02664763.2021.1911967
PubMed Central reference number	PMC10128098
Issue Number	3
PubMed reference number	37114090
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2021-04-06
Access Restriction	Open
Rights License	© 2021 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	Distributed scheme hypothesis testing fraud detection classification
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty