In this work we propose a novel unsupervised classification technique 5 based on the estimation of nonlinear nonparametric mixed-effects models. The proposed method is an iterative algorithm that alternates a nonparametric EM step and a nonlinear Maximum Likelihood step.We apply this new procedure to perform an unsupervised clustering of longitudinal data in two different case studies.

A New Unsupervised Classification Technique Through Nonlinear Non Parametric Mixed-Effects Models / L. Azzimonti, F. Ieva, A.M. Paganoni - In: Complex Models and Computational Methods in Statistics / [a cura di] M. Grigoletto, F. Lisi, S. Petrone. - [s.l] : Springer, 2013. - ISBN 978-88-470-2870-8. - pp. 1-11 [10.1007/978-88-470-2871-5_1]

A New Unsupervised Classification Technique Through Nonlinear Non Parametric Mixed-Effects Models

F. Ieva
Secondo
;
2013

Abstract

In this work we propose a novel unsupervised classification technique 5 based on the estimation of nonlinear nonparametric mixed-effects models. The proposed method is an iterative algorithm that alternates a nonparametric EM step and a nonlinear Maximum Likelihood step.We apply this new procedure to perform an unsupervised clustering of longitudinal data in two different case studies.
Settore SECS-S/01 - Statistica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/233636
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