Equivalence of Gaussian measures for some nonstationary random fields
The author shows that results on equivalence for two stationary Gaussian random field models extend in a natural way to the equivalence of a stationary model and a power law model. This result is used to show that if we use a power law model for predicting a random field at unobserved locations when in fact the random field is stationary, we can obtain asymptotically optimal predictions as long as the high frequency behavior of the true spectral density is sufficiently close to the high frequency behavior of the spectral density of the power law model.
scientific article; zbMATH DE number 2105702