©2003 Marcel Dekker, Inc. All rights reserved. This material may not be used or reproduced in any form without the express written permission of Marcel Dekker, Inc. MARCEL DEKKER, INC. • 270 MADISON AVENUE • NEW YORK, NY 10016 COMMUNICATIONS IN STATISTICS Theory and Methods Vol. 32, No. 3, pp. 555–571, 2003 Indicator Variables in Spatial Prediction When  Is Unknown Felipe Peraza and Graciela Gonza´lez-Farı´as * Centro de Investigacio´n en Matema´ticas, Guanajuato, Me´xico ABSTRACT The use of indicator variables for computing predictions for the linear model is a well known technique. Fuller (Fuller, W. A. (1980). The use of indicator variables in computing predictions. J. Econometrics 2: 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. *Correspondence: Graciela Gonza´lez-Farı´as, Centro de Investigacio´n en Matema´ticas, Guanajuato, Me´xico; E-mail: farias@cimat.mx. 555 DOI: 10.1081/STA-120018551 0361-0926 (Print); 1532-415X (Online) Copyright & 2003 by Marcel Dekker, Inc. www.dekker.com