Frequentist standard errors of Bayes estimators

Publication Type:
Journal Article
Computational Statistics, 2017, 32 (3), pp. 867 - 888
Issue Date:
Filename Description Size
Lee2017_Article_FrequentistStandardErrorsOfBay.pdfPublished Version959 kB
Adobe PDF
Full metadata record
© 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.
Please use this identifier to cite or link to this item: