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In Bayesian prediction of a Gaussian space-time model, unknown parameters are considered as random variables with known prior distributions and, then the posterior and Bayesian predictive distributions are approximated with discritization method. Since prior distributions are often unknown, in this paper, parametric priors are considered. Then the empirical Bayes approach is used to estimate the prior distributions. Replacing these estimates in the Bayesian predictive distribution, an empirical Bayes space-time predictor and prediction variance are determined. Then an environmental example is used to illustrate the application of the proposed method. Finally the accuracy of the empirical Bayes space-time predictor is considered with cross validation criterion.

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In this paper, the worst allocation of deductibles  and limits in layer policies are discussed from the viewpoint  of the insurer. It is shown that if n independent and identically distributed exponential risks are covered by the layer policies and  the policy limits are equal, then the worst allocation of deductibles from the viewpoint of the insurer is (d‎, ‎0‎, ‎..., ‎0)‎.