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Model Projections in Model Space: A Geometric Interpretation of the AIC Allows Estimating the Distance Between Truth
José Miguel Ponciano1, Mark L Taper1,2
1Biology Department, University of Florida, Gainesville, FL, United States.
Frontiers in Ecology and Evolution
|April 2, 2021
Summary
New methods in ecological science improve model selection. This approach quantifies how close the best model is to the actual data-generating process, offering more accurate insights than traditional information criteria.
Area of Science:
- Ecology
- Statistical modeling
Background:
- Information criteria are vital for selecting probabilistic models in ecological science.
- Current methods identify the best model but not its proximity to the true data-generating process.
Purpose of the Study:
- To extend Akaike's geometric interpretation for a more comprehensive model selection.
- To develop a method for estimating the closeness of the best model to the data-generating process.
Main Methods:
- Utilizing divergences between models and the generating process.
- Incorporating estimable divergence relationships among approximating models.
- Employing an estimator of negative self-entropy to construct a model space.
Main Results:
- A novel model space construction that includes an estimated location for the generating process.
- The ability to determine both the best model and its estimated distance from the generating process.
- Demonstrated more accurate estimation of generating process properties compared to model averaging.
Conclusions:
- The proposed model projection method enhances ecological model selection accuracy.
- This approach provides a quantitative measure of model fit to the data-generating process.
- Applicable to all scientific fields using information criteria for model selection.
Keywords:
Akaike’s Information CriterionKullback-Leibler divergenceerror rates in model selectionmodel averagingmodel projectionsMore Related Videos
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