ArticleComputational brain & behavior2019
Rejoinder: More Limitations of Bayesian Leave-One-Out Cross-Validation.
Article in Computational brain & behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- BHCox: Bayesian heredity-constrained Cox proportional hazards models for detecting gene-environment interactions.BMC bioinformatics · 2025Article
- Bayes Factors for Mixed Models: Perspective on Responses.Computational brain & behavior · 2023Article
- Bayesian model-averaged meta-analysis in medicine.Statistics in medicine · 2021Article
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2 authors.
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Abstract
We recently discussed several limitations of Bayesian leave-one-out cross-validation (LOO) for model selection. Our contribution attracted three thought-provoking commentaries. In this rejoinder, we address each of the commentaries and identify several additional limitations of LOO-based methods such as Bayesian stacking. We focus on differences between LOO-based methods versus approaches that consistently use Bayes' rule for both parameter estimation and model comparison. We conclude that LOO-based methods do not align satisfactorily with the epistemic goal of mathematical psychology.
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