Local Influence For Elliptical Partially Varying-Coefficient Model

In this article, we extend varying-coefficient models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. This class of models includes all symmetric continuous distributions, such as Student-t, Pearson VII, power exponential and logistic, among others. Estimation is performed by maximum penalized likelihood method and by using smoothing splines. In order to study the sensitivity of the penalized estimates under some usual perturbation schemes in the model or data, the local influence curvatures are derived and some diagnostic graphics are proposed. A real dataset previously analysed by using varying-coefficient models with normal errors is reanalysed under varying-coefficient models with heavy-tailed errors.