The spatial random-effects model is flexible in modeling spatial covariance functions,and is computationally efficient for spatial prediction via fixed rank kriging.However,the model depends on a class of basis functions,which if not selected properly,may result in unstable or undesirable results.Additionally,the maximum likelihood(ML)estimates of the model parameters are commonly computed using an expectation-maximizatin(EM)algorithm,which further limits its applicability when a large number of basis functions are required.