Paper Info
Title
Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory
Abstract
Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory, called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.
Year
DOI
Venue
2014
10.1109/FUZZ-IEEE.2014.6891608
FUZZ-IEEE
Keywords
Field
DocType
fuzzy set theory,credibility theory,type-2 fuzzy set,expected value,type-2 fuzzy regression model,regression analysis,vague data expression,regression model,membership function,t2f expected value regression model,arbitrage pricing theory,type-1 fuzzy set,pricing,uncertainty,linear regression,mathematical model,data models
Fuzzy regression model,Econometrics,Computer science,Regression analysis,Fuzzy measure theory,Credibility theory,Expected value,Fuzzy number,Arbitrage pricing theory,Membership function
Conference
ISSN
Citations 
PageRank 
1544-5615
0
0.34
References 
Authors
10
2
Name
Order
Citations
PageRank
Yicheng Wei131.42
Junzo Watada241184.53