Regression Model Based on Fuzzy Random Variables

Authors:
Shinya Imai;Shuming Wang;Junzo Watada
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan 808-0135;Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan 808-0135;Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan 808-0135
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Year:
2008

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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.