Random effects clustering in multilevel modeling: choosing a proper partition

Claudio Conversano (), Massimo Cannas, Francesco Mola and Emiliano Sironi
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Claudio Conversano: University of Cagliari
Massimo Cannas: University of Cagliari
Francesco Mola: University of Cagliari
Emiliano Sironi: Catholic University of Milan

Advances in Data Analysis and Classification, 2019, vol. 13, issue 1, No 12, 279-301

Abstract: Abstract A novel criterion for estimating a latent partition of the observed groups based on the output of a hierarchical model is presented. It is based on a loss function combining the Gini income inequality ratio and the predictability index of Goodman and Kruskal in order to achieve maximum heterogeneity of random effects across groups and maximum homogeneity of predicted probabilities inside estimated clusters. The index is compared with alternative approaches in a simulation study and applied in a case study concerning the role of hospital level variables in deciding for a cesarean section.

Keywords: Hierarchical modelling; Model based clustering; Label switching; Bayesian nonparametric; Gini income inequality ratio; Goodman and Kruskal predictability index; 62C10; 62C12; 62H30; 62J12; 62J20 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11634-018-0347-9

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