The use of approximate methods as the INLA (Integrated Nested Laplace Approximation) approach is being widely used in Bayesian inference, especially in spatial risk model estimation where the Besag-York-Mollie (BYM) model ` has found a proper use. INLA appears time saving compared to Monte Carlo simulations based on Markov Chains (MCMC), but it produces some differences in estimates [1, 2]. Data from the Veneto Cancer Registry has been considered with the scope to compare cancer incidence estimates with INLA method and with two other procedures based on MCMC simulation, WinBUGS and CARBayes, under R environment. It is noteworthy that INLA returns estimates comparable to both MCMC procedures, but it appears sensitive to the a-priori distribution. INLA is fast and efficient in particular with samples of moderate-high size. However, care must to be paid to the choice of the parameter relating to the a-priori distribution.

Assessment of the INLA approach on gerarchic bayesian models for the spatial disease distribution: a real data application

Girardi Paolo;
2018

Abstract

The use of approximate methods as the INLA (Integrated Nested Laplace Approximation) approach is being widely used in Bayesian inference, especially in spatial risk model estimation where the Besag-York-Mollie (BYM) model ` has found a proper use. INLA appears time saving compared to Monte Carlo simulations based on Markov Chains (MCMC), but it produces some differences in estimates [1, 2]. Data from the Veneto Cancer Registry has been considered with the scope to compare cancer incidence estimates with INLA method and with two other procedures based on MCMC simulation, WinBUGS and CARBayes, under R environment. It is noteworthy that INLA returns estimates comparable to both MCMC procedures, but it appears sensitive to the a-priori distribution. INLA is fast and efficient in particular with samples of moderate-high size. However, care must to be paid to the choice of the parameter relating to the a-priori distribution.
Book of short papers - SIS 2018
File in questo prodotto:
File Dimensione Formato  
1314-2677-1-PB.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Dominio pubblico
Dimensione 6.53 MB
Formato Adobe PDF
6.53 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3757429
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact