Nonlinear nonparametric mixed-effects models for unsupervised classification

In this work we propose a novel EM method for the estimation of nonlinear nonparametric mixed-effects models, aimed at unsupervised classification. We perform simulation studies in order to evaluate the algorithm performance and we apply this new procedure to a real dataset. Copyright Springer-Verlag 2013

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Year of publication:	2013
Authors:	Azzimonti, Laura ; Ieva, Francesca ; Paganoni, Anna Maria
Published in:	Computational Statistics. - Springer. - Vol. 28.2013, 4, p. 1549-1570
Publisher:	Springer
Subject:	Mixed-effects models \| Nonparametric estimation \| EM algorithm \| Nonlinear models

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010698287