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. (C) 2009 Elsevier Inc. All rights reserved.
Multi-sample test-based clustering for fuzzy random variables / G., Gonzales Rodriguez; Ana, Colubi; D'Urso, Pierpaolo; Manuel, Montenegro. - In: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING. - ISSN 0888-613X. - 50:5(2009), pp. 721-731. [10.1016/j.ijar.2009.01.003]
Multi-sample test-based clustering for fuzzy random variables
D'URSO, Pierpaolo;
2009
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. (C) 2009 Elsevier Inc. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
