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Local influence diagnostics for hierarchical finite-mixture random-effects models.

Trias Wahyuni Rakhmawati1, Geert Molenberghs1,2, Geert Verbeke1,2

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Summary
This summary is machine-generated.

This study introduces a new diagnostic tool to assess subject influence in mixed-effects models with mixture distributions. The method helps identify how individual subjects impact model results, particularly concerning mixture component probabilities.

Keywords:
local influencemixture model for random-effectsperturbation

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Repeated measures data analysis often employs random-effects models.
  • Mixture distributions are used when random effects exhibit complex patterns.
  • Assessing individual subject influence is crucial for model diagnostics.

Purpose of the Study:

  • To evaluate the influence of individual subjects on random-effects models with mixture distributions.
  • To develop a diagnostic tool based on local influence and perturbation schemes.
  • To compare the proposed perturbation scheme with existing methods.

Main Methods:

  • Utilizing a local influence diagnostic tool.
  • Implementing a perturbation scheme targeting mixture component probabilities.
  • Comparing with a method perturbing subject likelihood contributions.
  • Applying linear mixed models to real-world data and simulations.

Main Results:

  • The proposed diagnostic tool effectively identifies influential subjects.
  • Perturbing mixture component probabilities offers distinct insights compared to perturbing subject likelihoods.
  • Case studies and simulations confirm the utility of the diagnostic approach.

Conclusions:

  • The developed local influence method provides valuable diagnostics for random-effects models with mixture distributions.
  • Understanding subject-specific influences is essential for robust statistical inference.
  • The study highlights the importance of targeted perturbation schemes in model diagnostics.