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A distance-based statistical analysis of fuzzy number-valued data

2013; Elsevier BV; Volume: 55; Issue: 7 Linguagem: Inglês

10.1016/j.ijar.2013.09.020

1873-4731

Ángela Blanco, María Rosa Casals, Ana Colubi, Norberto Corral, Marta García-Bárzana, Marı́a Ángeles Gil, Gil González–Rodríguez, Marı́a Teresa López, Marı́a Asunción Lubiano, Manuel Montenegro, Ana Belén Ramos-Guajardo, Sara de la Rosa de Sáa, Beatriz Sinova,

Rough Sets and Fuzzy Logic

Real-life data associated with experimental outcomes are not always real-valued. In particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature, especially when they come from human valuations. Fuzzy numbers have extensively been considered to provide us with a convenient tool to express these vague data. In analyzing fuzzy data from a statistical perspective one finds two key obstacles, namely, the nonlinearity associated with the usual arithmetic with fuzzy data and the lack of suitable models and limit results for the distribution of fuzzy-valued statistics. These obstacles can be frequently bypassed by using an appropriate metric between fuzzy data, the notion of random fuzzy set and a bootstrapped central limit theorem for general space-valued random elements. This paper aims to review these ideas and a methodology for the statistical analysis of fuzzy number data which has been developed along the last years.