### 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)≡R^{2}| 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 language | English |
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Title of host publication | Studies in Computational Intelligence |

Pages | 153-170 |

Number of pages | 18 |

Volume | 313 |

DOIs | |

Publication status | Published - 2010 |

### Publication series

Name | Studies in Computational Intelligence |
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Volume | 313 |

ISSN (Print) | 1860949X |

### Fingerprint

### ASJC Scopus subject areas

- Artificial Intelligence

### Cite this

*Studies in Computational Intelligence*(Vol. 313, pp. 153-170). (Studies in Computational Intelligence; Vol. 313). https://doi.org/10.1007/978-3-642-15220-7_13

**Some examples of computing the possibilistic correlation coefficient from joint possibility distributions.** / Fullér, Robert; Mezei, József; Várlaki, Péter.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Studies in Computational Intelligence.*vol. 313, Studies in Computational Intelligence, vol. 313, pp. 153-170. https://doi.org/10.1007/978-3-642-15220-7_13

}

TY - CHAP

T1 - Some examples of computing the possibilistic correlation coefficient from joint possibility distributions

AU - Fullér, Robert

AU - Mezei, József

AU - Várlaki, Péter

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78049279892&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78049279892&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15220-7_13

DO - 10.1007/978-3-642-15220-7_13

M3 - Chapter

SN - 9783642152191

VL - 313

T3 - Studies in Computational Intelligence

SP - 153

EP - 170

BT - Studies in Computational Intelligence

ER -