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Space–time modelling of coupled spatiotemporal environmental variables
Author(s) -
Ippoliti L.,
Valentini P.,
Gamerman D.
Publication year - 2012
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2011.01011.x
Subject(s) - markov chain monte carlo , computer science , kalman filter , inference , probabilistic logic , state space representation , state variable , monte carlo method , markov chain , state space , algorithm , data mining , bayesian probability , mathematics , machine learning , statistics , artificial intelligence , physics , thermodynamics
Summary. The paper is concerned with a dynamic factor model for spatiotemporal coupled environmental variables. The model is proposed in a state space formulation which, through Kalman recursions, allows a unified approach to prediction and estimation. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo algorithms for dynamic linear models to our model formulation. The predictive ability of the model is discussed for two different data sets with variables measured at two different scales. Some possibilities for further research are also outlined.