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Computational Statistics

, Volume 28, Issue 4, pp 1549–1570 | Cite as

Nonlinear nonparametric mixed-effects models for unsupervised classification

  • Laura Azzimonti
  • Francesca Ieva
  • Anna Maria Paganoni
Original Paper

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.

Keywords

Mixed-effects models Nonparametric estimation EM algorithm Nonlinear models 

Notes

Acknowledgments

The case study in Sect. 4 is within the Strategic Program “Exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction” supported by “Ministero del Lavoro, della Salute e delle Politiche Sociali” and by “Direzione Generale Sanità - Regione Lombardia”.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Laura Azzimonti
    • 1
  • Francesca Ieva
    • 1
  • Anna Maria Paganoni
    • 1
  1. 1.MOX, Dipartimento di MatematicaPolitecnico di Milano MilanItaly

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