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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Change-Point Detection in a High-Dimensional Multinomial Sequence Based on Mutual Information.

Xinrong Xiang1, Baisuo Jin1, Yuehua Wu2

  • 1School of Management, University of Science and Technology of China, Heifei 230026, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for detecting structural changes in multinomial time-series data. The proposed approach accurately identifies change-points and estimates their locations, enhancing data analysis in various fields.

Keywords:
change-pointhigh-dimensional multinomial sequencelikelihood ratiomutual information

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Area of Science:

  • Statistics
  • Data Science
  • Time-Series Analysis

Background:

  • Time-series data frequently exhibit structural changes at unpredictable points.
  • Detecting these change-points is crucial for accurate data interpretation and modeling.

Purpose of the Study:

  • To propose a novel statistic for detecting change-points in multinomial sequences.
  • To provide a method for estimating the location of these change-points.

Main Methods:

  • A new statistic is developed based on mutual information after pre-classification.
  • The statistic is designed for multinomial sequences where category count approaches sample size.

Main Results:

  • The proposed statistic demonstrates high statistical power for change-point detection.
  • Accurate estimation of change-point locations is achieved.
  • The statistic exhibits asymptotic normality under the null hypothesis and consistency under the alternative.

Conclusions:

  • The novel statistic offers a robust method for identifying structural breaks in multinomial time-series data.
  • The technique is effective for both hypothesis testing and parameter estimation.
  • The method's utility is validated through simulations and a real-world physical examination dataset.