A hierarchical Bayesian factor model for multivariate spatially and temporally correlated data is proposed. This method searches factor scores incorporating a dependence within observations due to both a geographical and a temporal structure and it is an extension of a model proposed by Mezzetti (2012) using the results of a separable covariance matrix for the spatial panel data as in Leorato and Mezzetti (2016). A Gibbs sampling algorithm is implemented to sample from the posterior distributions. We illustrate the benefit and the performance of our model by analyzing death rates for different diseases together with some socio-economical and behavioural indicators and by analyzing simulated data.

Leorato, S., Mezzetti, M. (2021). A Bayesian factor model for spatial panel data with a separable covariance approach. BAYESIAN ANALYSIS, 16(2), 489-519 [10.1214/20-BA1215].

A Bayesian factor model for spatial panel data with a separable covariance approach

Mezzetti, M
2021-06-01

Abstract

A hierarchical Bayesian factor model for multivariate spatially and temporally correlated data is proposed. This method searches factor scores incorporating a dependence within observations due to both a geographical and a temporal structure and it is an extension of a model proposed by Mezzetti (2012) using the results of a separable covariance matrix for the spatial panel data as in Leorato and Mezzetti (2016). A Gibbs sampling algorithm is implemented to sample from the posterior distributions. We illustrate the benefit and the performance of our model by analyzing death rates for different diseases together with some socio-economical and behavioural indicators and by analyzing simulated data.
giu-2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-S/01 - STATISTICA
Settore STAT-01/A - Statistica
English
Con Impact Factor ISI
Bayesian inference; correlated factor loadings; factor analysis; autoregressive model; spatial data
Leorato, S., Mezzetti, M. (2021). A Bayesian factor model for spatial panel data with a separable covariance approach. BAYESIAN ANALYSIS, 16(2), 489-519 [10.1214/20-BA1215].
Leorato, S; Mezzetti, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/274989
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