首页|Spatiotemporal modelling with dynamic deformation for nonstationary covariance structures
Spatiotemporal modelling with dynamic deformation for nonstationary covariance structures
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NETL
NSTL
Oxford Univ Press
In this paper, we present an innovative spatiotemporal model that allows dynamic variation in the spatial correlation structure over time through dynamic deformation. We propose that temporal deformation occurs smoothly relative to that in the original region. To incorporate this idea, we employ state space models to model dynamic deformation. Generalizing this class of models based on spatial deformation was driven by the need to model monthly average temperature data in the southern region of Brazil. The distinctive traits of this region, characterized by plateaus and mountain ranges and close proximity to the Atlantic Ocean, provide notable geographic diversity. This diversity, in addition to different meteorological phenomena over time, may influence the spatial correlation function. The model parameters are estimated via a Bayesian approach, which requires the use of Markov chain Monte Carlo methods to approximate the posterior distributions of parameters. The model is applied to 15 years of monthly average temperature data from the southern region of Brazil. The primary result of this analysis reveals a significant improvement in temperature modelling when the proposed model is used compared with that when versions that employ static deformation are used.
Bayesian inferencedynamic spatial deformationtemperature data analysisspatiotemporal modelling
Fidel Ernesto Castro Morales、Marina Silva Paez
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Department of Statistics, Federal University of Rio Grande do Norte, Rio Grande do Norte, Natal, Brazil
Department of Statistical Methods, Institute of Mathematics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
NETL
NSTL
Oxford Univ Press
