Title: Robustness for multilevel models with the forward search
Authors: Luigi Grossi - University of Verona (Italy) [presenting]
Aldo Corbellini - Faculty of Economics - University of Parma (Italy)
Fabrizio Laurini - University of Parma (Italy)
Abstract: Robustness of standard regression models have been studied quite extensively. When repeated measures are available, the methodological framework is generalized to multilevel models, for which little is known in term of robustness, even in the simplest case of ANOVA. We present a sequential forward search algorithm for multilevel models that allows robust and efficient parameters estimation in presence of outliers, and it avoids masking and swamping. The influence of outliers, if any is inside the dataset, will be monitored at each step of the sequential procedure, which is the key element of the forward search. There are peculiar features when the forward search is applied to multilevel models. Such features pose new computational challenges, as some restrictions, that make the sub-models identifiable at every step. Preliminary results on simulated data have highlighted the benefit of adopting the forward search algorithm, which can reveal masked outliers, influential observations and show hidden structures. An application to real data is also illustrated, where trades of coffee to European countries are analyzed to identify outliers that might be linked to potential frauds.