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.

Nonlinear nonparametric mixed-effects models for unsupervised classification / L. Azzimonti, F. Ieva, A. Maria Paganoni. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 28:4(2013 Aug), pp. 1549-1570. [10.1007/s00180-012-0366-5]

Nonlinear nonparametric mixed-effects models for unsupervised classification

F. Ieva
Secondo
;
2013

Abstract

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.
Mixed-effects models ; Nonparametric estimation ; EM algorithm ; Nonlinear models
Settore SECS-S/01 - Statistica
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/233392
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