An interchangeable approach for modelling spatio-temporal count data

作者：Chiogna Monica^*; Gaetan Carlo

来源：Environmetrics, 2010, 21(7-8): 844-862.

DOI：10.1002/env.1078

• hierarchical models
• dynamic models
• random fields
• MCMC inference

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.

• 出版日期2010-12