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Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA


Held, L; Schrödle, B; Rue, H (2010). Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA. In: Kneib, T; Tutz, G. Statistical Modelling and Regression Structures - Festschrift in Honour of Ludwig Fahrmeir. Berlin: Physica-Verlag (Springer), 91-110.

Abstract

Model criticism and comparison of Bayesian hierarchical models is often
based on posterior or leave-one-out cross-validatory predictive checks. Crossvalidatory
checks are usually preferred because posterior predictive checks are difficult
to assess and tend to be too conservative. However, techniques for statistical
inference in such models often try to avoid full (manual) leave-one-out crossvalidation,
since it is very time-consuming. In this paper we will compare two approaches
for estimating Bayesian hierarchical models: Markov chain Monte Carlo
(MCMC) and integrated nested Laplace approximations (INLA). We review how
both approaches allow for the computation of leave-one-out cross-validatory checks
without re-running the model for each observation in turn.We then empirically compare
the two approaches in an extensive case study analysing the spatial distribution
of bovine viral diarrhoe (BVD) among cows in Switzerland.

Abstract

Model criticism and comparison of Bayesian hierarchical models is often
based on posterior or leave-one-out cross-validatory predictive checks. Crossvalidatory
checks are usually preferred because posterior predictive checks are difficult
to assess and tend to be too conservative. However, techniques for statistical
inference in such models often try to avoid full (manual) leave-one-out crossvalidation,
since it is very time-consuming. In this paper we will compare two approaches
for estimating Bayesian hierarchical models: Markov chain Monte Carlo
(MCMC) and integrated nested Laplace approximations (INLA). We review how
both approaches allow for the computation of leave-one-out cross-validatory checks
without re-running the model for each observation in turn.We then empirically compare
the two approaches in an extensive case study analysing the spatial distribution
of bovine viral diarrhoe (BVD) among cows in Switzerland.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2010
Deposited On:14 Dec 2010 12:39
Last Modified:05 Apr 2016 14:17
Publisher:Physica-Verlag (Springer)
ISBN:978-3-7908-2412-4
Publisher DOI:https://doi.org/10.1007/978-3-7908-2413-1
Related URLs:http://opac.nebis.ch/F?func=direct&local_base=NEBIS&doc_number=006000767

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