GLS DISCREPANCY BASED INFORMATION CRITERIA FOR SELECTING COVARIANCE STRUCTURE MODELS

Abstract

This paper studies an information criterion for selecting covariance structure models using the generalized least squares (GLS) procedure. A risk assessed by the predictive GLS discrepancy function is introduced and used to determine the quality of a model. By correcting the biases in the sample GLS discrepancy function, four GLS discrepancy based information criteria are proposed. Monte Carlo results illustrate the merits of each criterion in model selection and in minimizing the risk.