Nonlinear nonparametric mixed-effects models for unsupervised classification
Monday 30th May 2011
Azzimonti, L.; Ieva, F.; Paganoni, A.M.
In this work we propose a novel estimation method for nonlinear nonparametric mixed-effects models, aimed at unsupervised classification. The proposed method is an iterative algorithm that alternates a nonparametric EM step and a nonlinear Maximum Likelihood step. We perform simulation studies in order to evaluate the algorithm performances and we apply this new procedure to a real dataset.