Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Computational Statistics, Volume 36
AbstractCross-validation can be used to measure a model’s predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV) requires that the observation model can be factorized into simple terms, but a lot of important models in temporal and spatial statistics do not have this property or are inefficient or unstable when forced into a factorized form. We derive how to efficiently compute and validate both exact and approximate LOO-CV for any Bayesian non-factorized model with a multivariate normal or Student-t distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.
Bayesian inference, Cross-validation, Non-factorized models, Pareto-smoothed importance-sampling, SAR models
Bürkner , P C , Gabry , J & Vehtari , A 2021 , ' Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models ' , Computational Statistics , vol. 36 , pp. 1243–1261 . https://doi.org/10.1007/s00180-020-01045-4