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Nested logistic regression models and ΔAUC applications: Change-point analysis.

Chun Yin Lee1

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Summary
This summary is machine-generated.

This study addresses challenges in evaluating predictive models using the change in area under the receiver operating characteristic curve (ΔAUC). A new statistical test is proposed for nested logistic models with change-point predictors, offering improved accuracy assessment.

Keywords:
Area under the receiver operating characteristic curvechange-pointsdiscriminatory measuresm-out-of-n bootstrapnested models

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • The area under the receiver operating characteristic curve (AUC) is a key metric for predictive model performance.
  • Change in AUC (ΔAUC) assesses added predictors' value in nested models.
  • Existing methods for ΔAUC are limited by non-normal distributions under certain conditions.

Purpose of the Study:

  • To review ΔAUC usage for nested logistic models and its distributional challenges.
  • To propose a new statistical test for nested logistic models with a change-point predictor.
  • To develop a resampling scheme for critical values and bootstrap inference for change-point parameters.

Main Methods:

  • Review of ΔAUC for nested logistic models.
  • Development of a novel test statistic for change-point models based on ΔAUC.
  • Implementation of a resampling scheme for critical value approximation.
  • Application of m-out-of-n bootstrap for change-point parameter inference.

Main Results:

  • Demonstration of ΔAUC's degeneracy and non-normal distribution issues.
  • Proposal of a new ΔAUC-based test statistic for change-point models.
  • Validation of the proposed method through large-scale simulations.
  • Application to real-life datasets for practical illustration.

Conclusions:

  • The proposed ΔAUC test effectively handles nested logistic models with change-point predictors.
  • The resampling scheme and bootstrap inference provide reliable critical values and parameter estimates.
  • The method offers a robust approach for evaluating predictive accuracy improvements in complex models.