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Model Selection and Multimodel Inference: A Practical Information-theoretic Approach
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Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). | Semantic Scholar
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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach | SpringerLink
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Multimodel Inference
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Information criteria for model selection - Zhang - WIREs Computational Statistics - Wiley Online Library
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model selection - Why is BIC overfitting for a large data set but not from a smaller sample? - Cross Validated
LM101-077: How to Choose the Best Model using BIC - Learning Machines 101
Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM) - ScienceDirect
