Displaying similar documents to “Fuzzy data in statistics”

On approximate fuzzy maps.

Salih, Haval Mahmood Mohammed, Borumand Saeid, A. (2010)

Acta Universitatis Apulensis. Mathematics - Informatics


On fuzzy quotient mappings.

Ramakrishnan, P.V., Regina Mary, A. (2005)

Bulletin of the Malaysian Mathematical Sciences Society. Second Series


Fuzzy numbers, definitions and properties.

Miguel Delgado, José Luis Verdegay, M. Amparo Vila (1994)

Mathware and Soft Computing


Two different definitions of a Fuzzy number may be found in the literature. Both fulfill Goguen's Fuzzification Principle but are different in nature because of their different starting points. The first one was introduced by Zadeh and has well suited arithmetic and algebraic properties. The second one, introduced by Gantner, Steinlage and Warren, is a good and formal representation of the concept from a topological point of view. The objective of this paper is...

A Method to Construct an Extension of Fuzzy Information Granularity Based on Fuzzy Distance

Thien, Nguyen Van, Demetrovics, Janos, Thi, Vu Duc, Giang, Nguyen Long, Son, Nguyen Nhu (2016)

Serdica Journal of Computing


In fuzzy granular computing, a fuzzy granular structure is the collection of fuzzy information granules and fuzzy information granularity is used to measure the granulation degree of a fuzzy granular structure. In general, the fuzzy information granularity characterizes discernibility ability among fuzzy information granules in a fuzzy granular structure. In recent years, researchers have proposed some concepts of fuzzy information granularity based on partial order relations. However,...

Goodman-Kruskal Measure of Association for Fuzzy-Categorized Variables

S. M. Taheri, Gholamreza Hesamian (2011)



The Goodman-Kruskal measure, which is a well-known measure of dependence for contingency tables, is generalized to the case when the variables of interest are categorized by linguistic terms rather than crisp sets. In addition, to test the hypothesis of independence in such contingency tables, a novel method of decision making is developed based on a concept of fuzzy p -value. The applicability of the proposed approach is explained using a numerical example.