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Change point detection for high dimensional data via kernel measure with application to human aging brain data.

Jinjuan Wang1, Na Li2, Zhen Meng3

  • 1School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China.

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|August 31, 2023
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Summary

This study introduces a kernel-based statistic (KUCP) for detecting change points in high-dimensional data without needing distributional assumptions or pre-estimated parameters. KUCP demonstrates superior sensitivity and accuracy in identifying and locating these critical data shifts.

Keywords:
change point detectiongene expression profilehigh dimensional datakernel-based method

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Area of Science:

  • Statistics
  • Data Science
  • Bioinformatics

Background:

  • Change point detection is crucial in various statistical applications.
  • Existing methods struggle with high-dimensional data due to restrictive distributional assumptions and parameter sensitivity.
  • Accurate identification of change points is vital for understanding complex data patterns.

Purpose of the Study:

  • To propose a novel kernel-based statistic (KUCP) for change point detection in high-dimensional data.
  • To develop a KUCP method that is free from distributional assumptions and pre-parameter estimations.
  • To enhance the sensitivity and accuracy of change point detection and localization.

Main Methods:

  • Developed a kernel-based statistic ( -statistic) utilizing kernel functions to measure subject similarities.
  • Constructed a statistical test to ascertain the existence of a change point at a specific location.
  • Implemented a dichotomy algorithm for sequential change point localization.
  • Deduced the asymptotic properties of the -statistic.

Main Results:

  • The proposed KUCP method shows improved sensitivity in detecting the existence of change points compared to existing methods.
  • KUCP exhibits higher accuracy in locating change points.
  • Simulations confirm the effectiveness and robustness of the KUCP approach.
  • The method was successfully applied to human brain gene expression data to identify aging-related changes.

Conclusions:

  • KUCP offers a powerful, assumption-free approach for change point detection in high-dimensional datasets.
  • The method provides a significant advancement over traditional techniques, particularly for complex biological data.
  • KUCP has practical implications for fields like bioinformatics and neuroscience, aiding in the analysis of aging-related gene expression patterns.