Abstract: This paper introduces a quasi-deviance function for model selection of given spatial data. The proposed deviance function involves only the mean and covariance of responses, and therefore avoids the difficulty of specifying a full-likelihood function. We show that, under certain regularity conditions, the deviance function with quasi-likelihood estimating equation has a limiting chi-squared distribution. The asymptotic quadratic form of the deviance function provides a consistent method for selecting the true model. We also conduct simulations to evaluate the performance of the proposed method, and use the East Lansing Woods data to illustrate the application.
Key words and phrases: Deviance function, model selection, quasi-likelihood estimate, spatial data.