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Improved Multivariate Prediction under a General Linear Model

Author

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  • Gotway, C. A.
  • Cressie, N.

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.

Suggested Citation

  • Gotway, C. A. & Cressie, N., 1993. "Improved Multivariate Prediction under a General Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 56-72, April.
    • Handle: RePEc:eee:jmvana:v:45:y:1993:i:1:p:56-72
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    Cited by:

    1. Chaturvedi, Anoop & Wan, Alan T. K. & Singh, Shri P., 2002. "Improved Multivariate Prediction in a General Linear Model with an Unknown Error Covariance Matrix," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 166-182, October.
    2. D. T. Nava & F. De Bastiani & M. A. Uribe-Opazo & O. Nicolis & M. Galea, 2017. "Local Influence for Spatially Correlated Binomial Data: An Application to the Spodoptera frugiperda Infestation in Corn," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 540-561, December.

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