Complex Models and Computational Methods in Statistics 123

نویسندگان

  • Matteo Grigoletto
  • Francesco Lisi
  • Sonia Petrone
چکیده

In this work we propose a novel unsupervised classification technique 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.

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تاریخ انتشار 2013