When intrinsic Gaussian random Markov field (IGRMF) priors are assumed for random effects of a latent Gaussian model, a notable issue concerns prior elicitation for the precision hyperparameters. In fact, the structure of the precision matrix could lead to the undesired feature that the same prior for different precisions imply different marginal priors for the random effects. The work is aimed at investigating this problem following a rigorous mathematical procedure, in order to propose a new strategy and compare it to a widespread solution based on matrix scaling. Finally, an application of the proposed method to a real data problem is presented.

A. Gardini, F.G. (2020). Priors on precision parameters of IGRMF models. Pearson.

Priors on precision parameters of IGRMF models

A. Gardini
;
F. Greco;C. Trivisano
2020

Abstract

When intrinsic Gaussian random Markov field (IGRMF) priors are assumed for random effects of a latent Gaussian model, a notable issue concerns prior elicitation for the precision hyperparameters. In fact, the structure of the precision matrix could lead to the undesired feature that the same prior for different precisions imply different marginal priors for the random effects. The work is aimed at investigating this problem following a rigorous mathematical procedure, in order to propose a new strategy and compare it to a widespread solution based on matrix scaling. Finally, an application of the proposed method to a real data problem is presented.
2020
Book of Short Papers SIS 2020
459
464
A. Gardini, F.G. (2020). Priors on precision parameters of IGRMF models. Pearson.
A. Gardini, F. Greco, C. Trivisano
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS 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: https://hdl.handle.net/11585/806924
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact