Abstract
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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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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Azzimonti, L., Ieva, F., Paganoni, A.M. (2013). A New Unsupervised Classification Technique Through Nonlinear Non Parametric Mixed-Effects Models. In: Grigoletto, M., Lisi, F., Petrone, S. (eds) Complex Models and Computational Methods in Statistics. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-2871-5_1
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DOI: https://doi.org/10.1007/978-88-470-2871-5_1
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