Abstract
The use of indicator variables for computing predictions for the linear model is a well known technique. Fuller (Fuller, W. A. (Citation1980). The use of indicator variables in computing predictions. J. Econometrics2: 231–243.) extends this to predictions for models with a general covariance structure and nonlinear models. In this work we use indicator variables for spatial data models with trend and a parametrized but unknown covariance function. We show that Restricted Maximum Likelihood (REML) estimates are a natural way to estimate the covariance parameters under this schema. We use dummy variables to predict the response at any number of sites, on a random Gaussian field. A simulation study was conducted to study the performance of the estimate and predictor when we consider indicator variables in the model.
Acknowledgments
We are grateful to S. Pantula for his comments and careful suggestions. This research is supported by Consejo Nacional de Ciencia y Tecnologi′a grant 32393-E. The authors also wish to express their gratitude to the editor and referee for their helpful comments.
Notes
aWe used our own code as well as commercial software with GLS routines for special covariance structures, and REML and ML methods for estimation of covariance parameters.