Publication Results

Code: 22/2011
Title: Nonlinear nonparametric mixed-effects models for unsupervised classification
Date: Monday 30th May 2011
Author(s) : Azzimonti, L.; Ieva, F.; Paganoni, A.m.
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Abstract: 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.