On Matched Filtering for Statistical Change Point Detection

On Matched Filtering for Statistical Change Point Detection

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms. However, randomness in the test statistic as a function of time makes them susceptible to false positives and localization ambiguity. We address these issues by deriving and appl...

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Bibliographic Details
Main Authors: Kevin C. Cheng, Eric L. Miller, Michael C. Hughes, Shuchin Aeron
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Change point detection
matched filter
Wasserstein distance
Kolmogorov-Smirnov
maximum mean discrepancy quantile-quantile tests
human activity data
Online Access:https://ieeexplore.ieee.org/document/9246231/

Internet

https://ieeexplore.ieee.org/document/9246231/

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