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Multiple Copula Regression Function and Directional Dependence Under Multivariate Non-exchangeable Copulas

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Causal Inference in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 622))

Abstract

In this paper, the multiple directional dependence between response variable and covariates using non-exchangeable copulas based regression is introduced. The general measure for the multiple directional dependence in the joint behavior is provided. Several multivariate non-exchangeable copula families including skew normal copula, and the generalized Farlie-Gumbel-Morgenstern copula models are investigated. For the illustration of main results, several examples are given.

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Authors and Affiliations

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

    Zheng Wei & Daeyoung Kim

  2. Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, USA

    Zheng Wei & Tonghui Wang

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

Correspondence to Tonghui Wang .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

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

    Vladik Kreinovich

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

    Songsak Sriboonchitta

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Wei, Z., Wang, T., Kim, D. (2016). Multiple Copula Regression Function and Directional Dependence Under Multivariate Non-exchangeable Copulas. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics. Studies in Computational Intelligence, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-27284-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-27284-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27283-2

  • Online ISBN: 978-3-319-27284-9

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