Conditional vs marginal estimation of the predictive loss of hierarchical models using WAIC and cross-validation

DOI10.1007/s11222-017-9736-8zbMath1384.62093OpenAlexW2589196999MaRDI QIDQ1702015

Russell B. Millar

Publication date: 27 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-017-9736-8

zbMATH Keywords

importance samplingcross-validationhierarchical modelleave-one-outBayesian modelsmodel comparisonWAICmarginalized likelihoodover-dispersed count datapointwise predictive lossWatanabe-Akaike information criterion

Mathematics Subject Classification ID

Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10) Statistical aspects of information-theoretic topics (62B10)

Related Items (6)

Bayesian model selection in linear mixed models for longitudinal data ⋮ Model selection for Bayesian linear mixed models with longitudinal data: Sensitivity to the choice of priors ⋮ Bayesian model selection for multilevel mediation models ⋮ Bayesian comparison of latent variable models: conditional versus marginal likelihoods ⋮ Bayesian model selection for longitudinal count data ⋮ Prediction scoring of data-driven discoveries for reproducible research

Cites Work

• Understanding predictive information criteria for Bayesian models
• Equations of states in singular statistical estimation
• A survey of Bayesian predictive methods for model assessment, selection and comparison
• Erratum to: ``Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC
• Algebraic Geometry and Statistical Learning Theory
• A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
• A Predictive Approach to Model Selection
• Bayesian Measures of Model Complexity and Fit
• Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities
• Methods and Criteria for Model Selection
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
• Assessment of locally influential observations in Bayesian models
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