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Considerations for assessing model averaging of regression coefficients.

Katharine M Banner1, Megan D Higgs1

  • 1Department of Mathematical Sciences, Montana State University, Wilson Hall 2-214, P.O. Box 172400, Bozeman, Montana, 59717, USA.

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|November 23, 2016
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Model averaging combines predictions from multiple statistical models, but averaging regression coefficients can lead to unclear inferences. Researchers should carefully consider model averaging

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Model choice uncertainty is inherent in statistical analyses.
  • Model averaging, initially for prediction, is increasingly used for explanatory inference.
  • Software accessibility and avoiding single model justification drive model averaging popularity.

Purpose of the Study:

  • To address the gap between theoretical model averaging and practical application.
  • To clarify potential inferential challenges when averaging regression coefficients.
  • To guide researchers in making informed decisions about using model averaging.

Main Methods:

  • Revisiting foundational regression modeling principles.
  • Proposing explicit notation and graphical tools for clarity.
  • Analyzing the process of combining individual model results for averaging.

Main Results:

  • Regression coefficients may lack equivalent interpretations across models, complicating inference.
  • Current practices in model averaging may lead to unclear or unjustified conclusions.
  • A need exists for clearer guidance on the appropriate use of model averaging.

Conclusions:

  • Model averaging requires careful consideration of parameter interpretability across models.
  • Researchers should prioritize question-focused modeling over method-focused approaches.
  • Improved notation and tools can enhance the understanding and application of model averaging.