Improved Multivariate Prediction under a General Linear Model

C. A. Gotway and N. Cressie

Journal of Multivariate Analysis, 1993, vol. 45, issue 1, 56-72

Abstract: Assuming a general linear model with known covariance matrix, several linear and nonlinear predictors are presented and their properties are discussed. In the context of simultaneous multiple prediction, a total sum of squared errors is suggested as a loss function for comparing predictors. Based on a rundamental relationship hetween prediction and estimation, a very general class of predictors is developed from which predictors with uniformly smaller risk than that of the classical best linear unbiased (i.e., universal kriging) predictor can be constructed.

Date: 1993
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