Longitudinal data analysis using t-type regression

We consider a robust estimator of linear regression for longitudinal data by maximizing marginal likelihood of a scaled t-type error distribution. The marginal likelihood can also be applied to the de-correlated response when the within-subject correlation can be consistently estimated from an initial estimate of the model based on the working assumption of independence. While the t-distributed errors can be motivated from a latent hierarchical model as an extension of Gaussian mixed models, our estimators have asymptotic normal distributions for a wider class of error distributions. The estimators have bounded influence functions and can achieve positive breakdown points regardless of the dimension of the covariates. (C) 2003 Elsevier B.V. All rights reserved.