Bayesian Local Influence for the Growth Curve Model with Raoʹs Simple Covariance Structure

In this paper, the Bayesian local influence approach is employed to diagnose the adequacy of the growth curve model with Raoʹs simple covariance structure, based on the Kullback–Leibler divergence. The Bayesian Hessian matrices of the model are investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is considered particularly and used to analyze two real-life biological data sets, which shows that the criteria presented in this article are useful in practice.