On spatiotemporal prediction for online monitoring dat

Spatiotemporal prediction is of interest in many areas of applied statistics, especially in environmental monitoring with online data information. At first, this article reviews the approaches for spatiotemporal modeling in the context of stochastic processes and then introduces the new class of spatiotemporal dynamic linear models. Further, the methods for linear spatial data analysis, universal kriging and trend surface analysis, are related to the method of spatial linear Bayesian analysis. The Kalman filter is the prefered method for temporal linear Bayesian inferences. By combining the Kalman filter recursions with the trend surface estimator and universal kriging predictor, the prior and posterior spatiotemporal predictors for the observational process are derived, which form the main result of this article. The problem of spatiotemporal linear prediction in the case of unknown first and second order moments is treated as well. (orig.)Available from TIB Hannover: RN 6363(1996,9) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman