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The Random Bernstein Polynomial Smoothing Via ABC Method

  • Leandro A. Ferreira
  • Victor Fossaluza
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 239)

Abstract

In recent years, many statistical inference problems have been solved by using Markov Chain Monte Carlo (MCMC) techniques. However, it is necessary to derivate the analytical form for the likelihood function. Although the level of computing has increased steadily, there is a limitation caused by the difficulty or the misunderstanding of how computing the likelihood function. The Approximate Bayesian Computation (ABC) method dispenses the use of the likelihood function by simulating candidates of posterior distributions and using an algorithm to accept or reject the proposed candidates. This work presents an alternative nonparametric estimation method of smoothing empirical distributions with random Bernstein polynomials via ABC method. The Bernstein prior is obtained by rewriting the Bernstein polynomial in terms of k mixtures / m mixtures of beta densities and mixing weights. A study of simulation and a real example are presented to illustrate the method proposed.

Keywords

Approximation Bayesian Computation Bayesian estimation Bernstein polynomials Nonparametric inference 

Notes

Acknowledgements

The authors are partially supported by CAPES grants.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departament of StatisticsUniversity of São PauloSão PauloBrazil

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