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Online multiple hypothesis testing.

David S Robertson1, James M S Wason2, Aaditya Ramdas3

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|January 15, 2024
PubMed
Summary
This summary is machine-generated.

This study reviews methods for controlling false discovery rates (FDR) in online hypothesis testing, where data arrives sequentially. It offers a comprehensive guide to theory, applications, and algorithms for managing statistical errors in real-time data streams.

Keywords:
A/B testingdata repositoriesplatform trialstype I error rate

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Area of Science:

  • Statistics
  • Data Science
  • Bioinformatics

Background:

  • Large-scale hypothesis testing is common in modern data analysis.
  • Controlling the false discovery rate (FDR) is crucial for reliable results.
  • Traditional FDR methods assume all data is available simultaneously, unsuitable for online, sequential testing.

Purpose of the Study:

  • To provide a comprehensive review of methodologies for online error rate control in multiple hypothesis testing.
  • To bridge the gap between traditional offline FDR control and the demands of sequential data analysis.
  • To offer insights into theoretical foundations, practical applications, and algorithmic comparisons for online hypothesis testing.

Main Methods:

  • Literature review of online error rate control methods developed over the past 15 years.
  • Exposition of key theoretical concepts in online multiple hypothesis testing.
  • Simulation studies comparing the performance of different online testing algorithms.

Main Results:

  • Identified and synthesized key advancements in online FDR control methodologies.
  • Demonstrated the practical applicability of online testing frameworks through applied examples.
  • Provided comparative performance analysis of various online hypothesis testing algorithms.

Conclusions:

  • Online error rate control is a critical and evolving field for sequential data analysis.
  • The reviewed methods offer effective strategies for managing statistical errors in real-time hypothesis testing.
  • This work serves as a valuable resource for researchers and practitioners applying these advanced statistical techniques.