Spatio-temporal count autoregression

Fuente: arXiv
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Main Authors: Maletz, Steffen, Fokianos, Konstantinos, Fried, Roland
Format: Preprint
Published: 2024
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author Maletz, Steffen
Fokianos, Konstantinos
Fried, Roland
author_facet Maletz, Steffen
Fokianos, Konstantinos
Fried, Roland
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2404_02982
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Spatio-temporal count autoregression
Maletz, Steffen
Fokianos, Konstantinos
Fried, Roland
Methodology
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.
title Spatio-temporal count autoregression
topic Methodology
url https://arxiv.org/abs/2404.02982