  An additive growth curve model with orthogonal design matrices is proposed in
which observations may have different profile forms. The proposed model allows
us to fit data and then estimate parameters in a more parsimonious way than the
traditional growth curve model. Two-stage generalized least-squares estimators
for the regression coefficients are derived where a quadratic estimator for the
covariance of observations is taken as the first-stage estimator. Consistency,
asymptotic normality and asymptotic independence of these estimators are
investigated. Simulation studies and a numerical example are given to
illustrate the efficiency and parsimony of the proposed model for model
specifications in the sense of minimizing Akaike's information criterion (AIC).
