Local Influence for elliptical partially verying-coefficient model / Sebastián Ignacio Reyes Bodelón.

Magíster en Estadística.

In this thesis we extend varying-coecient 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 in uence curvatures are derived and some diagnostic graphics are proposed. A real data set previously analyzed by using varying-coecient models with normal errors is reanalyzed under varying-coecient models with heavy-tailed errors. Keywords: Partially varying-coecients models, Maximum penalized likelihood estimates, Robust estimates, Sensitivity analysis.