Spatio-Temporal Count Autoregression

Spatio-Temporal Count Autoregression

We study the problem of modeling and inference for spatio-temporal count processes. Our approach uses parsimonious parameterisations of multivariate autoregressive count time series models, including possible regression on covariates. We control the number of parameters by specifying spatial neighbo...

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Bibliographic Details
Published in:Data science in science Vol. 3; no. 1
Main Authors: Maletz, Steffen, Fokianos, Konstantinos, Fried, Roland
Format: Journal Article
Language:English
Published: Taylor & Francis Group 31-12-2024
Subjects:
covariates
generalized linear models
INGARCH
log-linear models
space time data
spatio-temporal autocovariance
Online Access:Get full text
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Summary:We study the problem of modeling and inference for spatio-temporal count processes. Our approach uses parsimonious parameterisations of multivariate autoregressive count time series models, including possible regression on covariates. We control the number of parameters by specifying spatial neighbourhood structures for possibly huge matrices that take into account spatio-temporal dependencies. This work is motivated by real data applications which call for suitable models. Extensive simulation studies show that our approach yields reliable estimators.
ISSN:2694-1899
2694-1899
DOI:10.1080/26941899.2024.2425171