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Metrika

pp 1–26 | Cite as

A general guide in Bayesian and robust Bayesian estimation using Dirichlet processes

  • Ali KarimnezhadEmail author
  • Mahmoud Zarepour
Article
  • 48 Downloads

Abstract

In this paper, we investigate Bayesian and robust Bayesian estimation of a wide range of parameters of interest in the context of Bayesian nonparametrics under a broad class of loss functions. Dealing with uncertainty regarding the prior, we consider the Dirichlet and the Dirichlet invariant priors, and provide explicit form of the resulting Bayes and robust Bayes estimators. Tractability of the results is supported by numerous examples of different well-known loss functions. The practical utility of the proposed Bayes and robust Bayes estimators are examined for a real data set.

Keywords

Bayesian estimation Bayesian nonparametrics Dirichlet process Dirichlet invariant process 

Notes

Acknowledgements

The authors are cordially grateful to the Editor in Chief and two anonymous reviewers for raising several helpful comments and suggestions which led to a substantial improvement in the quality of our work. Research of the second author was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2018-04008).

Compliance with ethical standards

Conflict of interest

The author declares that there is no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biochemistry, Microbiology and Immunology, Faculty of MedicineUniversity of OttawaOttawaCanada
  2. 2.Department of StatisticsUniversity of OttawaOttawaCanada

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