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An interchangeable approach for modelling spatio‐temporal count data
We describe a model‐based approach to analyse space–time count data. Such data can arise as a number of time series of counts, each representing a specific geographical area, i.e. as spatial time series, or as a number of spatial maps at different time points, i.e. as temporal spatial processes. We propose a Bayesian hierarchical formulation capable of embracing both cases, with principal kriging functions combined with latent parameters having prior distributions able to deal with spatial/temporal dependence. The methodology is applied to monitoring problems in environmental and epidemiological applications. Copyright © 2010 John Wiley & Sons, Ltd.
An interchangeable approach for modelling spatio‐temporal count data
We describe a model‐based approach to analyse space–time count data. Such data can arise as a number of time series of counts, each representing a specific geographical area, i.e. as spatial time series, or as a number of spatial maps at different time points, i.e. as temporal spatial processes. We propose a Bayesian hierarchical formulation capable of embracing both cases, with principal kriging functions combined with latent parameters having prior distributions able to deal with spatial/temporal dependence. The methodology is applied to monitoring problems in environmental and epidemiological applications. Copyright © 2010 John Wiley & Sons, Ltd.
An interchangeable approach for modelling spatio‐temporal count data
Chiogna, Monica (author) / Gaetan, Carlo (author)
Environmetrics ; 21 ; 849-867
2010-11-01
19 pages
Article (Journal)
Electronic Resource
English
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