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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered

Xiaoping Shi1, Yuehua Wu2, Calyampudi Radhakrishna Rao3,4

  • 1Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, BC, Canada V2C0C8; xshi@tru.ca wuyh@mathstat.yorku.ca crr1@psu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|May 23, 2018
PubMed
Summary

This study introduces a novel non-Euclidean shortest Hamiltonian path (SHP) test for change-point detection, outperforming existing Euclidean methods. This robust approach accurately identifies critical time points even with random data interference.

Keywords:
change-pointdistribution-freeminimum spanning treenon-Euclidean distanceshortest Hamilton path

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Area of Science:

  • Statistics
  • Data Analysis
  • Network Science

Background:

  • Change-point detection methods using Euclidean minimum spanning trees (MST) and shortest Hamiltonian paths (SHP) have limitations with random interferences.
  • Existing Euclidean graph-based tests may fail in noisy datasets, impacting reliability.

Purpose of the Study:

  • To develop a powerful and robust non-Euclidean shortest Hamiltonian path (SHP)-based test for change-point detection.
  • To address the limitations of Euclidean methods in the presence of random interferences.

Main Methods:

  • A novel non-Euclidean SHP-based test was developed, designed to be consistent and distribution-free.
  • The performance of the new test was evaluated through simulations and compared against Euclidean MST and SHP methods, as well as a non-Euclidean MST approach.

Main Results:

  • The proposed non-Euclidean SHP test demonstrated superior power compared to all tested Euclidean and non-Euclidean MST-based methods.
  • The test proved effective in identifying critical time points, such as landing and departure times, in video data of bee flower visits.

Conclusions:

  • The non-Euclidean SHP test offers a more powerful and reliable solution for change-point detection, especially in datasets with random interferences.
  • This method has practical applications in analyzing complex time-series data, including biological observations.