Some examples of computing the possibilistic correlation coefficient from joint possibility distributions

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Abstract

In this paper we will show some examples for computing the possibilistic correlation coefficient between marginal distributions of a joint possibility distribution. First we consider joint possibility distributions, (1-x-y), (1-x 2-y 2), and (1-x 2-y) on the set {(x,y)≡R2| x≥0,y≥0,x+y≤1}, then we will show (i) how the possibilistic correlation coefficient of two linear marginal possibility distributions changes from zero to -1/2, and from -1/2 to -3/5 by taking out bigger and bigger parts from the level sets of a their joint possibility distribution; (ii) how to compute the autocorrelation coefficient of fuzzy time series with linear fuzzy data.

Original languageEnglish
Title of host publicationComputational Intelligence in Engineering
EditorsImre Rudas
Pages153-170
Number of pages18
DOIs
Publication statusPublished - Nov 3 2010

Publication series

NameStudies in Computational Intelligence
Volume313
ISSN (Print)1860-949X

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Fullér, R., Mezei, J., & Várlaki, P. (2010). Some examples of computing the possibilistic correlation coefficient from joint possibility distributions. In I. Rudas (Ed.), Computational Intelligence in Engineering (pp. 153-170). (Studies in Computational Intelligence; Vol. 313). https://doi.org/10.1007/978-3-642-15220-7_13