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Approximate Bayesian Methods for Multivariate and Conditional Copulae

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Soft Methods for Data Science (SMPS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

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Abstract

We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution, where copula is a flexible probabilistic tool that allows the researcher to model the joint distribution of a random vector in two separate steps: the marginal distributions and a copula function which captures the dependence structure among the vector components. The method is also based on the properties of an approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighted in terms of their empirical likelihood. This method is particularly useful when the likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.

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References

  1. Sklar M (1959) Fonctions de répartition à \(n\) dimensions et leurs marges. Publ Inst Statist Univ Paris 8:229–231

    MathSciNet  MATH  Google Scholar 

  2. Craiu VR, Sabeti A (2012) In mixed company: Bayesian inference for bivariate conditional copula models with discrete and continuous outcomes. J Multivar Anal 110:106–120

    Google Scholar 

  3. Mengersen K, Pudlo P, Robert CP (2013) Bayesian computation via empirical likelihood. Proc Natl Acad Sci 110(4):1321–1326

    Google Scholar 

  4. Min A, Czado C (2010) Bayesian inference for multivariate copulas using pair-copula constructions. J Financ Econometrics 8(4):511–546

    Article  Google Scholar 

  5. Owen AB (2010) Empirical likelihood. Chapman & Hall/CRC Press, New York

    MATH  Google Scholar 

  6. Schmid F, Schmidt R (2007) Multivariate extensions of Spearmans Rho and related statistics. Stat Probab Lett 77(4):407–416

    Article  MathSciNet  MATH  Google Scholar 

  7. Gijbels I, Veraverbeke N, Omelka M (2011) Conditional copulas, association measures and their applications. Comput Stat Data Anal 55(5):1919–1932

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. MEMOTEF, Sapienza Università di Roma, Roma, Italy

    Clara Grazian & Brunero Liseo

Authors
  1. Clara Grazian
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  2. Brunero Liseo
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Corresponding author

Correspondence to Brunero Liseo .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  2. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  3. Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  4. Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  5. Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  6. Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  7. Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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© 2017 Springer International Publishing Switzerland

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Grazian, C., Liseo, B. (2017). Approximate Bayesian Methods for Multivariate and Conditional Copulae. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

  • eBook Packages: EngineeringEngineering (R0)

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