期刊
COMPUTATIONAL STATISTICS
卷 36, 期 2, 页码 1243-1261出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-020-01045-4
关键词
Cross-validation; Pareto-smoothed importance-sampling; Non-factorized models; Bayesian inference; SAR models
资金
- Aalto University
This study introduces a method to efficiently compute and validate exact and approximate LOO-CV for any Bayesian non-factorized model with a multivariate normal or Student-t distribution. The method is demonstrated using lagged simultaneously autoregressive (SAR) models as a case study.
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 distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据