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Advances in Data Analysis and Classification

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12-10-2018 | Regular Article

Random effects clustering in multilevel modeling: choosing a proper partition

Authors: Claudio Conversano, Massimo Cannas, Francesco Mola, Emiliano Sironi

Published in: Advances in Data Analysis and Classification | Issue 1/2019

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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.

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Title: Random effects clustering in multilevel modeling: choosing a proper partition
Authors: Claudio Conversano
Massimo Cannas
Francesco Mola
Emiliano Sironi
Publication date: 12-10-2018
Publisher: Springer Berlin Heidelberg
Published in: Advances in Data Analysis and Classification / Issue 1/2019
Print ISSN: 1862-5347
Electronic ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-018-0347-9

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