Article2003

Approximate cross‐validatory predictive checks in disease mapping models

E Clare Marshall · David J. Spiegelhalter

143 citations0/0 files storedStatistics in Medicine

No readable copy stored yet — this record is metadata only.

Spatial and Panel Data Analysis Statistical Methods and Bayesian Inference Markov Chains and Monte Carlo Methods

Abstract

When fitting complex hierarchical disease mapping models, it can be important to identify regions that diverge from the assumed model. Since full leave-one-out cross-validatory assessment is extremely time-consuming when using Markov chain Monte Carlo (MCMC) estimation methods, Stern and Cressie consider an importance sampling approximation. We show that this can be improved upon through replication of both random effects and data. Our approach is simple to apply, entirely generic, and may aid the criticism of any Bayesian hierarchical model.

Citations in this library

Stored only when both papers are in the corpus — 143 citations exist upstream.

References

1995On conditional and intrinsic autoregressions 1993Spatial Correlation in Ecological Analysis 2000Bayesian Detection of Clusters and Discontinuities in Disease Maps 2000Posterior predictive model checks for disease mapping models

Cited by

2010Using Integrated Nested Laplace Approximations for the Evaluation of Veterinary Surveillance Data from Switzerland: A Case-Study 2021Methods to assess evidence consistency in dose‐response model based network meta‐analysis 2014Explaining variations in obesity and inactivity between US metropolitan areas

Imported from OpenAlex (CC0) — nothing here is generated.