Modeling of Spatio-Temporal Data with Non-Ignorable Missing

Often, due to conditions under which measurements are made, spatio-temporal data contain missing values. Missing data in spatial or temporal vicinity may include useful information. Using this information, we can provide more accurate results, so missing data should be carefully examined. By modeling the missing process and spatio-temporal measurement process jointly, some lost information could be recovered. In this paper, we implement joint modeling in a Bayesian framework using the "shared parameter model" technique, so that the bad effects of missing values will be moderated. Also, we will associate these two processes via a latent spatio-temporal random field. To estimate the model parameters and for predictions, the Bayesian method INLA using SPDE approach is applied. Also, the lake surface water temperature data for Caspian sea is used to evaluate the performance of the joint model.