A Comparison of REML and Covariance Adjustment Method in the Estimation of Growth Curve Models

The classical growth curve model is considered when one continuous characteristic is measured at q time points. The covariance adjusted estimator of growth curve parameters is the OLS estimator adjusted using analysis of covariance. The covariates are obtained from functions of within individuals error contrasts. On the other hand, REML estimators emerge from maximization of the likelihood of OLS residuals. We compare the efficiency of estimators of growth curve parameters obtained by REML with that of covariance-adjusted least squares estimators with covariates selected via CAIC.