An important problem in statistics is the study of spatio-te
mporal data taking into account the
effect of explanatory variables such as latitude, longitud
e and time. In this paper, a new Bayesian
approach for analyzing spatial longitudinal data is propos
ed. It takes into account linear time
regression structures on the mean and linear regression str
uctures on the variance-covariance
matrix of normal observations. The spatial structure is inc
luded in the time regression parameters
and also in the regression structure of the variance covaria
nce matrix. Initially, we present a
summary of the spatial models and the Bayesian methodology u
sed to fit the models, as a
extension of the longitudinal data analysis. Next, the gene
ral spatial temporal model is proposed.
Finally, this proposal is used to study rainfall dataPeer Reviewe
