Frequentist standard errors of Bayes estimators

Lee, DH; Carroll, RJ; Sinha, S

Frequentist standard errors of Bayes estimators

Lee, DH Carroll, RJ

Sinha, S

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Publication Type:: Journal Article
Citation:: Computational Statistics, 2017, 32 (3), pp. 867 - 888
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Lee, DH	en_US
dc.contributor.author	Carroll, RJ https://orcid.org/0000-0002-5465-9682	en_US
dc.contributor.author	Sinha, S	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	Computational Statistics, 2017, 32 (3), pp. 867 - 888	en_US
dc.identifier.issn	0943-4062	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127053
dc.description.abstract	© 2017, Springer-Verlag Berlin Heidelberg. Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.	en_US
dc.relation.ispartof	Computational Statistics	en_US
dc.relation.isbasedon	10.1007/s00180-017-0710-x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Frequentist standard errors of Bayes estimators	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	32	en_US
utslib.for	0104 Statistics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

© 2017, Springer-Verlag Berlin Heidelberg. Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.

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http://hdl.handle.net/10453/127053