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Knockoff procedure for false discovery rate control in high-dimensional data streams.

Ka Wai Tsang1, Fugee Tsung2, Zhihao Xu3

  • 1School of Data Science, The Chinese University of Hong Kong, Shenzhen Guangdong 518172, People's Republic of China.

Journal of Applied Statistics
|October 9, 2023
PubMed
Summary

This study introduces a new Knockoff filtering procedure for identifying faulty data streams in statistical process control (SPC). The method effectively controls false discoveries while maintaining high power, even with limited out-of-control samples.

Keywords:
Fault identificationknockoff filteringmultiple testingmultistage manufacturing processstatistical process control

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Area of Science:

  • Statistics
  • Industrial Engineering
  • Data Science

Background:

  • Root-cause identification in high-dimensional data streams is crucial for fault detection.
  • Limited samples of out-of-control (OC) data streams pose challenges for traditional methods.
  • Controlling false discoveries is essential for reliable fault identification in statistical process control (SPC).

Purpose of the Study:

  • To propose a novel Knockoff procedure for multiple testing in multivariate SPC.
  • To develop a method that combines with existing fault detection techniques without altering stopping times.
  • To control the false discovery rate (FDR) in identifying OC data streams.

Main Methods:

  • A Knockoff filtering procedure is proposed for multivariate SPC.
  • The procedure is designed to integrate with other fault detection methods.
  • Theoretical guarantees for FDR control are provided via a theorem.

Main Results:

  • The proposed Knockoff procedure effectively controls the false discovery rate (FDR).
  • The method demonstrates high statistical power in identifying faulty data streams.
  • Simulation studies validate the performance and FDR control.

Conclusions:

  • The Knockoff procedure offers a robust solution for fault identification in SPC with limited OC samples.
  • This method enhances the reliability of fault detection in high-dimensional data streams.
  • The approach is applicable to real-world scenarios, such as semiconductor manufacturing.