Posterior expectation based on empirical likelihoods

Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James-Stein estimation. We use the Laplace method to establish asymptotic approximations to...

Full description

Saved in:
Bibliographic Details
Published in:Biometrika 2014-09, Vol.101 (3), p.711-718
Main Authors: VEXLER, A., TAO, G., HUTSON, A. D.
Format: Article
Language:English
Subjects:
Approximation
Asymptotic methods
Bayes estimators
Bayesian analysis
Empiricism
Estimating techniques
Estimation methods
Estimators
Information economics
Laplaces method
Mathematical models
Maximum likelihood estimators
Miscellanea
Parameter estimation
Point estimators
Scientific method
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James-Stein estimation. We use the Laplace method to establish asymptotic approximations to our proposed posterior expectations, and show by simulation that they are often more efficient than the corresponding classical nonparametric procedures, especially when the underlying data are skewed.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asu018