Bootstrap approach to the multi-sample test of means with imprecise data

Authors:
María Ángeles Gil;Manuel Montenegro;Gil González-Rodríguez;Ana Colubi;María Rosa Casals
Affiliations:
Departamento de Estadística, I.O. y D.M., Universidad de Oviedo, 33071 Oviedo, Spain;Departamento de Estadística, I.O. y D.M., Universidad de Oviedo, 33071 Oviedo, Spain;Departamento de Estadística, I.O. y D.M., Universidad de Oviedo, 33071 Oviedo, Spain;Departamento de Estadística, I.O. y D.M., Universidad de Oviedo, 33071 Oviedo, Spain;Departamento de Estadística, I.O. y D.M., Universidad de Oviedo, 33071 Oviedo, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

A bootstrap approach to the multi-sample test of means for imprecisely valued sample data is introduced. For this purpose imprecise data are modelled in terms of fuzzy values. Populations are identified with fuzzy-valued random elements, often referred to in the literature as fuzzy random variables. An example illustrates the use of the suggested method. Finally, the adequacy of the bootstrap approach to test the multi-sample hypothesis of means is discussed through a simulation comparative study.