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A distributed multiple sample testing for massive data.

Xie Xiaoyue1,2, Jian Shi1,2, Kai Song3

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing, People's Republic of China.

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|April 28, 2023
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Summary
This summary is machine-generated.

This study introduces a distributed two-node hypothesis testing method for large datasets, enhancing statistical inference accuracy in distributed systems while addressing privacy and communication challenges.

Keywords:
Distributed schemeclassificationfraud detectionhypothesis testing

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Area of Science:

  • Statistics
  • Computer Science
  • Data Science

Background:

  • Traditional hypothesis testing is challenging in distributed data storage due to communication costs and privacy issues.
  • Existing methods struggle with large-scale distributed datasets, limiting statistical inference accuracy.

Purpose of the Study:

  • To develop and investigate a distributed two-node hypothesis testing scheme using a divide-and-conquer strategy.
  • To apply this scheme for distributed fraud detection and distribution-based classification in multi-node systems.
  • To enhance the accuracy of statistical inference within distributed storage architectures.

Main Methods:

  • A novel distributed two-node Kolmogorov-Smirnov hypothesis testing scheme.
  • Implementation via a divide-and-conquer strategy for efficient data processing.
  • Development of distributed fraud detection and classification algorithms leveraging the proposed testing scheme.

Main Results:

  • The proposed distributed hypothesis testing scheme effectively addresses communication and privacy concerns.
  • Demonstrated feasibility of distributed fraud detection to identify nodes with fraudulent data.
  • Successful implementation of distribution-based classification for multi-node machine analysis.
  • Improved accuracy of statistical inference in distributed environments.

Conclusions:

  • The developed distributed hypothesis testing framework is effective for large-scale data analysis.
  • The methods offer practical solutions for fraud detection and data classification in distributed systems.
  • Simulation and real-world studies confirm the viability and benefits of the proposed approaches.