Applications of Graph-Theoretic Tests to Online Change Detection

Abstract

Detecting change in a stochastic process is a central problem in statistics. This project explores nonparametric graph-theoretic approaches to solving online change-point problems. The foundation for our methodology is the Ensemble Sum of Pair-Maxima (ESPM) Test, a powerful offline test developed by Ruth and Koyak (2011). Our work investigates the efficacy of the ESPM Test in a variety of offline settings, and ultimately extends that test to online settings through a novel modification of recently developed multiple testing procedures designed to control false discovery rate. When tested against simulated and pseudo real-world data, this modified procedure maintains the desired overall test level while achieving impressive power and useful advanced warning times in many scenarios. This method is not limited to the ESPM test and holds much promise for adapting other powerful offline techniques to online scenarios.

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Document Details

Document Type
Technical Report
Publication Date
May 09, 2014
Accession Number
ADA604776

Entities

People

  • Colin E. Bodgan

Organizations

  • United States Naval Academy

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Change Detection
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Detection
  • Differential Equations
  • Information Science
  • Measurement
  • Normal Distribution
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistics
  • Stochastic Processes
  • United States Naval Academy

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