Multi-sample test-based clustering for fuzzy random variables

Authors:
Gil González-Rodríguez;Ana Colubi;Pierpaolo D'Urso;Manuel Montenegro
Affiliations:
Research Unit on Intelligent Data Analysis and Graphical Models, European Centre for Soft Computing, 33600 Mieres, Spain;Dpto. de Estadística e I.O. y D.M., Universidad de Oviedo, C/Calvo sotelo s/n, 33007 Oviedo, Spain;Dipartimento di Teoria economica e metodi quantitativi per le scelte politiche, Sapienza Università di Roma, P.za Aldo Moro, 5-00185 Rome, Italy;Dpto. de Estadística e I.O. y D.M., Universidad de Oviedo, C/Calvo sotelo s/n, 33007 Oviedo, Spain
Venue:
International Journal of Approximate Reasoning
Year:
2009

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Abstract

A clustering method to group independent fuzzy random variables observed on a sample by focusing on their expected values is developed. The procedure is iterative and based on the p-value of a multi-sample bootstrap test. Thus, it simultaneously takes into account fuzziness and stochastic variability. Moreover, an objective stopping criterion leading to statistically equal groups different from each other is provided. Some simulations to show the performance of this inferential approach are included. The results are illustrated by means of a case study.