Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory

Yicheng Wei, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    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.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2368-2375
    Number of pages8
    ISBN (Print)9781479920723
    DOIs
    Publication statusPublished - 2014 Sep 4
    Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing
    Duration: 2014 Jul 62014 Jul 11

    Other

    Other2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
    CityBeijing
    Period14/7/614/7/11

    Fingerprint

    Credibility Theory
    Fuzzy Regression
    Arbitrage
    Fuzzy Model
    Pricing
    Regression Model
    Fuzziness
    Model-based
    Membership Function
    Uncertainty
    Fuzzy Sets
    Fuzzy sets
    Type-2 Fuzzy Sets
    Costs
    Vagueness
    Membership functions
    Ambiguous
    Expected Value
    Express
    Model

    Keywords

    • Credibility theory
    • expected value
    • regression model
    • Type-2 fuzzy set

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

    Cite this

    Wei, Y., & Watada, J. (2014). Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. In IEEE International Conference on Fuzzy Systems (pp. 2368-2375). [6891608] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2014.6891608

    Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. / Wei, Yicheng; Watada, Junzo.

    IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2368-2375 6891608.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Wei, Y & Watada, J 2014, Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. in IEEE International Conference on Fuzzy Systems., 6891608, Institute of Electrical and Electronics Engineers Inc., pp. 2368-2375, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, 14/7/6. https://doi.org/10.1109/FUZZ-IEEE.2014.6891608
    Wei Y, Watada J. Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. In IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc. 2014. p. 2368-2375. 6891608 https://doi.org/10.1109/FUZZ-IEEE.2014.6891608
    Wei, Yicheng ; Watada, Junzo. / Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 2368-2375
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