Longitudinal data with nonstationary errors:: a nonparametric three-stage approach

We develop here a three-stage nonparametric method to estimate the common, group and individual effects in a longitudinal data setting. Our three-stage additive model assumes that the dependence between performance in an audiologic test and time is a sum of three components. One of them is the same for all individuals, the second one corresponds to the group effect and the last one to the individual effects. We estimate these functional components by nonparametric kernel smoothing techniques. We give theoretical results concerning rates of convergence of our estimates. This method is then applied to the data set that motivated the methods proposed here, the speech recognition data from the Iowa Cochlear Implant Project.