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Approximate Bayesian Computation for Copula Estimation

Author

Listed:
  • Clara Grazian

    (Università di Roma "La Sapienza", Italy)

  • Brunero Liseo

    (Università di Roma "La Sapienza", Italy)

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 and it is based on the properties of an Approximate Bayesian Monte\,Carlo algorithm, where the proposed values of the functional of interest are weighed in terms of their empirical likelihood. This method is particularly useful when the 'true' likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.

Suggested Citation

  • Clara Grazian & Brunero Liseo, 2015. "Approximate Bayesian Computation for Copula Estimation," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 111-127.
    • Handle: RePEc:bot:rivsta:v:75:y:2015:i:1:p:111-127
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    Cited by:

    1. Kristin McCullough & Tatiana Dmitrieva & Nader Ebrahimi, 2022. "New approximate Bayesian computation algorithm for censored data," Computational Statistics, Springer, vol. 37(3), pages 1369-1397, July.

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