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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Exact change point detection with improved power in small-sample binomial sequences.

David Ellenberger1, Berthold Lausen2, Tim Friede1

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Biometrical Journal. Biometrische Zeitschrift
|November 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel change point test for binomial sequences, improving power and accuracy, especially with small sample sizes. The new exact segmentation procedure offers a reliable method for detecting probability shifts in data.

Keywords:
Worsley's testbinary segmentationchange pointdisorder detectionexact test

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Existing methods for detecting changes in binomial sequences have limitations, particularly with small sample sizes or low event rates.
  • Asymptotic approximations can be inaccurate, while exact methods may be overly conservative due to discrete test statistics.

Purpose of the Study:

  • To develop a more powerful and accurate change point test for sequences of independent binomial random variables.
  • To extend existing methods to reduce discreteness in test statistics and improve power, especially in small sample scenarios.

Main Methods:

  • The study extends Worsley and Halpern's approaches, incorporating binary segmentation concepts.
  • A novel exact segmentation procedure is developed, deriving exact distributions under specific conditions.
  • The procedure adds an ordering to equally valued test statistics, utilizing unused information in binomial sequences.

Main Results:

  • The proposed exact segmentation procedure constructs a change point test that controls the type-I error rate at any nominal level.
  • The new test is proven to be uniformly at least as powerful as Worsley's exact test.
  • Monte Carlo simulations show remarkable power gains, particularly in small sample size scenarios.

Conclusions:

  • The developed exact segmentation procedure provides a powerful and reliable method for change point detection in binomial data.
  • The test demonstrates wide-ranging applicability, as shown in examples involving clinical pin site infections and publication bias in drug research.