Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk - Šaltytė, J.; Dučinskas, K. (Nonlinear Analysis: Modelling and Control, 2002)
The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.