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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
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Statistical inference on change points in generalized semiparametric segmented models.

Guangyu Yang1, Baqun Zhang2, Min Zhang3

  • 1Institute of Statistics and Big Data, Renmin University of China, Beijing, 100872, China.

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|March 12, 2025
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Summary
This summary is machine-generated.

This study introduces a new statistical framework for detecting and estimating change points in segmented models. The method accurately identifies significant change-point effects in scientific data, offering clinically meaningful insights.

Keywords:
breakpointgeneralized linear spline modelknotnon-linear model

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Segmented models are crucial in scientific research, especially when change-point effects are present.
  • Accurate detection and estimation of these change points are vital for reliable analysis.

Purpose of the Study:

  • To propose a comprehensive semiparametric framework for testing the existence and estimating the location of change points in segmented models.
  • To provide a robust method for generalized outcome settings.

Main Methods:

  • A semismooth estimating equation approach for change-point estimation.
  • An average score-type test for hypothesis testing.
  • Rigorous theoretical analysis of estimators' consistency, normality, and efficiency.

Main Results:

  • The proposed framework demonstrates root-n consistency, asymptotic normality, and asymptotic efficiency for parameter estimators.
  • The distribution of the average score-type test statistics under the null hypothesis is rigorously derived.
  • Extensive simulations confirm the numerical performance of the estimation and testing methods.

Conclusions:

  • The developed semiparametric framework effectively tests for and estimates change points in segmented models.
  • Application to real-world data identified significant change-point effects, providing clinically relevant insights into factors influencing post-intervention bleeding.