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Related Experiment Videos

Local influence to detect influential data structures for generalized linear mixed models.

M J Ouwens1, F E Tan, M P Berger

  • 1Department of Methodology and Statistics, Maastricht University, The Netherlands. mario.ouwens@stat.unimaas.nl

Biometrics
|January 5, 2002
PubMed
Summary
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This study extends local influence measures for generalized linear models with random effects, proposing a two-step diagnostic approach to identify influential subjects and observations.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Local influence measures are crucial for identifying outliers in statistical models.
  • Existing methods primarily focus on normally distributed responses.
  • Generalized linear models with random effects are widely used in various scientific fields.

Purpose of the Study:

  • To generalize local influence measures for generalized linear models with random effects.
  • To propose a novel observation-oriented influence measure.
  • To introduce a two-step diagnostic procedure for detecting influential subjects and observations.

Main Methods:

  • Generalization of local influence measures.
  • Development of an observation-oriented influence measure.

Related Experiment Videos

  • Formulation of a two-step diagnostic strategy: subject influence followed by observation influence.

    • Main Results:

    • The subject-oriented influence measure is demonstrated to be a specific instance of the proposed observation-oriented measure.
    • The proposed two-step diagnostic procedure effectively identifies influential subjects and observations.
    • The study highlights the importance of detecting both influential subjects and observations.

    Conclusions:

    • The proposed methodology provides a robust framework for influence diagnostics in generalized linear models with random effects.
    • The two-step approach enhances the reliability of statistical analyses by identifying critical data points.
    • Practical application in crossover trials underscores the clinical relevance of these diagnostic tools.