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Formal Inference From More Than One Model: Multimodel Inference (MMI)

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Abstract

Model selection is most often thought of as a way to select just the best model, then inference is conditional on that model. However, information-theoretic approaches are more general than this simplistic concept of model selection. Given a set of models, specified independently of the sample data, we can make formal inferences based on the entire set of models. Here, the conditioning is on all the models in the set and this has several advantages; however, it does reinforce the importance of having a good set of models to carefully represent the scientific hypotheses of interest. Part of multimodel inference includes ranking the fitted models from best to worst, based on the Δi values, and then scaling to obtain the relative plausibility of each fitted model (g i ) by a weight of evidence (w i ) relative to the selected best model. Using the conditional sampling variance (var(|x, gi)) from each model and the Akaike weights (w i ), unconditional inferences about precision can be made over the entire set of models. Model-averaged parameter estimates and estimates of unconditional sampling variances can be easily computed. Model selection uncertainty is a substantial subject in its own right, well beyond just the issue of determining the best model.

Keywords

  • Model Selection
  • Model Average
  • Bootstrap Sample
  • Akaike Weight
  • Formal Inference

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-0-387-22456-5_4
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© 2002 Springer-Verlag New York, Inc.

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(2002). Formal Inference From More Than One Model: Multimodel Inference (MMI). In: Burnham, K.P., Anderson, D.R. (eds) Model Selection and Multimodel Inference. Springer, New York, NY. https://doi.org/10.1007/978-0-387-22456-5_4

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  • DOI: https://doi.org/10.1007/978-0-387-22456-5_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95364-9

  • Online ISBN: 978-0-387-22456-5

  • eBook Packages: Springer Book Archive