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A Multivariate Generalized FGM Copulas and Its Application to Multiple Regression

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Robustness in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 692))

Abstract

We introduce a class of multivariate non-exchangeable copulas which generalizes many known bivariate FGM type copula families. The properties such as moments, affiliation, association, and positive lower orthant dependent of the proposed class of copula are studied. The simple-to-use multiple regression function and multiple dependence measure formula for this new class of copulas are derived. Several examples are given to illustrate the main results obtained in this paper.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, USA

    Zheng Wei

  2. Department of Mathematics and Statistics, Organization University of Massachusetts Amherst, Amherst, USA

    Daeyoung Kim

  3. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Tonghui Wang & Teerawut Teetranont

Authors
  1. Zheng Wei
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  2. Daeyoung Kim
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  3. Tonghui Wang
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  4. Teerawut Teetranont
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Corresponding author

Correspondence to Zheng Wei .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso Department of Computer Science, El Paso, TX, USA

    Vladik Kreinovich

  2. Faculty of Economics, Chiang Mai University Faculty of Economics, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. Japan Adv. Inst. of Sci. & Tech. (JAIST) , Ishikawa, Japan

    Van-Nam Huynh

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Wei, Z., Kim, D., Wang, T., Teetranont, T. (2017). A Multivariate Generalized FGM Copulas and Its Application to Multiple Regression. In: Kreinovich, V., Sriboonchitta, S., Huynh, VN. (eds) Robustness in Econometrics. Studies in Computational Intelligence, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-319-50742-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-50742-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50741-5

  • Online ISBN: 978-3-319-50742-2

  • eBook Packages: EngineeringEngineering (R0)

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