Nonlinear nonparametric mixed-effects models for unsupervised classification

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.