Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models

Paul-Christian Bürkner (), Jonah Gabry and Aki Vehtari
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Paul-Christian Bürkner: Aalto University
Jonah Gabry: Columbia University
Aki Vehtari: Aalto University

Computational Statistics, 2021, vol. 36, issue 2, No 20, 1243-1261

Abstract: Abstract Cross-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- $$t$$ t distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.

Keywords: Cross-validation; Pareto-smoothed importance-sampling; Non-factorized models; Bayesian inference; SAR models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-01045-4

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