### Abstract

In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems. The objective of this paper is to build a regression model based on fuzzy random variables. First, a general regression model for fuzzy random data is proposed. After that, using expected value operators of fuzzy random variables, an expected regression model is established. The expected regression model can be developed by converting the original problem to a task of a linear programming problem. Finally, an explanatory example is provided.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 127-135 |

Number of pages | 9 |

Volume | 5179 LNAI |

Edition | PART 3 |

DOIs | |

Publication status | Published - 2008 |

Event | 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb Duration: 2008 Sep 3 → 2008 Sep 5 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 3 |

Volume | 5179 LNAI |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 |
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City | Zagreb |

Period | 08/9/3 → 08/9/5 |

### Fingerprint

### Keywords

- Expected value
- Fuzzy random variable
- Fuzzy regression model

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(PART 3 ed., Vol. 5179 LNAI, pp. 127-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5179 LNAI, No. PART 3). https://doi.org/10.1007/978-3-540-85567-5-17

**Regression model based on fuzzy random variables.** / Imai, Shinya; Wang, Shuming; Watada, Junzo.

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

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*PART 3 edn, vol. 5179 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 5179 LNAI, pp. 127-135, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, Zagreb, 08/9/3. https://doi.org/10.1007/978-3-540-85567-5-17

}

TY - GEN

T1 - Regression model based on fuzzy random variables

AU - Imai, Shinya

AU - Wang, Shuming

AU - Watada, Junzo

PY - 2008

Y1 - 2008

N2 - In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems. The objective of this paper is to build a regression model based on fuzzy random variables. First, a general regression model for fuzzy random data is proposed. After that, using expected value operators of fuzzy random variables, an expected regression model is established. The expected regression model can be developed by converting the original problem to a task of a linear programming problem. Finally, an explanatory example is provided.

AB - In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems. The objective of this paper is to build a regression model based on fuzzy random variables. First, a general regression model for fuzzy random data is proposed. After that, using expected value operators of fuzzy random variables, an expected regression model is established. The expected regression model can be developed by converting the original problem to a task of a linear programming problem. Finally, an explanatory example is provided.

KW - Expected value

KW - Fuzzy random variable

KW - Fuzzy regression model

UR - http://www.scopus.com/inward/record.url?scp=57749189766&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57749189766&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-85567-5-17

DO - 10.1007/978-3-540-85567-5-17

M3 - Conference contribution

AN - SCOPUS:57749189766

SN - 3540855661

SN - 9783540855668

VL - 5179 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 127

EP - 135

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -