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Nonlinear nonparametric mixed-effects models for unsupervised classification

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  • Laura Azzimonti
  • Francesca Ieva
  • Anna Maria Paganoni

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. Copyright Springer-Verlag 2013

Suggested Citation

  • Laura Azzimonti & Francesca Ieva & Anna Maria Paganoni, 2013. "Nonlinear nonparametric mixed-effects models for unsupervised classification," Computational Statistics, Springer, vol. 28(4), pages 1549-1570, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1549-1570
    DOI: 10.1007/s00180-012-0366-5
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    References listed on IDEAS

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    1. Jeroen K. Vermunt, 2004. "An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 220-233, May.
    2. Alison L. Gibbs & Francis Edward Su, 2002. "On Choosing and Bounding Probability Metrics," International Statistical Review, International Statistical Institute, vol. 70(3), pages 419-435, December.
    3. Tze Leung Lai, 2003. "Nonparametric estimation in nonlinear mixed effects models," Biometrika, Biometrika Trust, vol. 90(1), pages 1-13, March.
    4. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
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    Cited by:

    1. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Elson Tomás & Susana Vinga & Alexandra M. Carvalho, 2017. "Unsupervised learning of pharmacokinetic responses," Computational Statistics, Springer, vol. 32(2), pages 409-428, June.
    3. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    4. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.

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