Efficient Bayesian estimation of spatial Poisson auto-regression with Leroux random effects
Efficient Bayesian estimation of spatial Poisson auto-regression with Leroux random effects
The standard Poisson Auto-Regression framework considers static coefficients and does not incorporate any spatio-temporal dependence on the parameters governing the process dynamic. However, unobserved space-time variability is a very relevant component when dealing with observations organised in space and time. We consider a more flexible specification that can adjust for local deviations from the general pattern while borrowing information from adjacent areas. The model, specified in a Bayesian framework, might suffer from computational bottlenecks that can make its estimation unfeasible. Therefore, we implement it in STAN to jointly update all the parameters and improve mixing, while adopting a novel sparse-matrix representation to attain improved computational performances. The computational advantage and the model performances have been validated through a simulation study.
14-19
Alaimo Di Loro, Pierfrancesco
be794c06-67df-4468-b7e1-f33181c58a5e
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
30 January 2025
Alaimo Di Loro, Pierfrancesco
be794c06-67df-4468-b7e1-f33181c58a5e
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Alaimo Di Loro, Pierfrancesco, Böhning, Dankmar and Sahu, Sujit K.
(2025)
Efficient Bayesian estimation of spatial Poisson auto-regression with Leroux random effects.
In,
Pollice, A. and Mariani, P.
(eds.)
Methodological and Applied Statistics and Demography III.
(Methodological and Applied Statistics and Demography III)
Springer Cham, .
(doi:10.1007/978-3-031-64431-3_3).
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Book Section
Abstract
The standard Poisson Auto-Regression framework considers static coefficients and does not incorporate any spatio-temporal dependence on the parameters governing the process dynamic. However, unobserved space-time variability is a very relevant component when dealing with observations organised in space and time. We consider a more flexible specification that can adjust for local deviations from the general pattern while borrowing information from adjacent areas. The model, specified in a Bayesian framework, might suffer from computational bottlenecks that can make its estimation unfeasible. Therefore, we implement it in STAN to jointly update all the parameters and improve mixing, while adopting a novel sparse-matrix representation to attain improved computational performances. The computational advantage and the model performances have been validated through a simulation study.
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Published date: 30 January 2025
Identifiers
Local EPrints ID: 506308
URI: http://eprints.soton.ac.uk/id/eprint/506308
ISSN: 3059-2135
PURE UUID: a8aab24a-f012-4aac-a43a-cc9061ef732e
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Date deposited: 04 Nov 2025 17:35
Last modified: 05 Nov 2025 02:44
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Contributors
Author:
Pierfrancesco Alaimo Di Loro
Editor:
A. Pollice
Editor:
P. Mariani
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