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Related Experiment Videos

Influence analysis for linear mixed-effects models.

Eugene Demidenko1, Therese A Stukel

  • 1Dartmouth Medical School, Hanover, NH 03755, USA. eugene.demidenko@dartmouth.edu

Statistics in Medicine
|November 24, 2004
PubMed
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This study introduces new regression diagnostics for linear mixed-effects models, simplifying influence analysis. These methods help identify influential data points without re-estimating the model, improving statistical model assessment.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Linear regression diagnostics are crucial for model interpretation.
  • Linear mixed-effects models are widely used but lack comprehensive diagnostic tools.
  • Existing methods for linear models do not directly apply to mixed-effects models.

Purpose of the Study:

  • To extend established linear regression diagnostic techniques to linear mixed-effects models.
  • To develop new influence measures with direct interpretations for mixed-effects models.
  • To provide efficient diagnostics that do not require model re-estimation.

Main Methods:

  • Generalization of Miller's update formula for case deletion diagnostics.
  • Adaptation of Pregibon's infinitesimal case deletion diagnostics.

Related Experiment Videos

  • Derivation of a matrix formula for local influence on fixed-effects coefficients.
  • Extension of leverage, Cook's distance, and local influence measures.

    • Main Results:

    • New diagnostic measures are proposed for linear mixed-effects models.
    • These measures collapse to familiar linear regression diagnostics when no random effects are present.
    • The new diagnostics are computationally efficient, avoiding model re-estimation.
    • A link between local influence and Cook's distance is established.

    Conclusions:

    • The developed diagnostics provide valuable tools for assessing influence in linear mixed-effects models.
    • These methods aid in identifying influential observations and their impact on model parameters.
    • The study offers practical applications, demonstrated through Medicare reimbursement data analysis.