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Bayesian and frequentist confidence intervals arising from empirical-type likelihoods
In Hong Chang,Rahul Mukerjee +1 more
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For a general class of empirical-type likelihoods for the population mean, higher-order asymp totics are developed with a view to characterizing its members which allow, for any given prior, the existence of a confidence interval that has approximately correct posterior as well as fre quentist coverage as mentioned in this paper.Abstract:
SUMMARY For a general class of empirical-type likelihoods for the population mean, higher-order asymp totics are developed with a view to characterizing its members which allow, for any given prior, the existence of a confidence interval that has approximately correct posterior as well as fre quentist coverage. In particular, it is seen that the usual empirical likelihood always allows such a confidence interval, while many of its variants proposed in the literature do not enjoy this property. An explicit form of the confidence interval is also given.read more
Citations
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Bayesian empirical likelihood for quantile regression
Yunwen Yang,Xuming He +1 more
TL;DR: Taking the empirical likelihood into a Bayesian framework, it is shown that the resultant posterior from any fixed prior is asymptotically normal; its mean shrinks toward the true parameter values, and its variance approaches that of the maximum empirical likelihood estimator.
Journal ArticleDOI
Bayesian Estimation and Comparison of Moment Condition Models
TL;DR: This article developed a Bayesian semiparametric analysis of moment condition models by casting the problem within the exponentially tilted empirical likelihood (ETEL) framework, and applied it to moment condition analysis.
Journal ArticleDOI
Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation
TL;DR: This work considers Bayesian empirical likelihood estimation and develops an efficient Hamiltonian Monte Carlo method for sampling from the posterior distribution of the parameters of interest and uses hitherto unknown properties of the gradient of the underlying log‐empirical‐likelihood function to show its utility.
Journal ArticleDOI
Objective Bayesian inference with proper scoring rules
TL;DR: In this paper, the authors proposed a scoring rule based posterior distribution for the unknown parameter of interest, which can be used to update the information provided by the scoring rule in the SR-posterior distribution.
Journal ArticleDOI
Bayesian semiparametric hierarchical empirical likelihood spatial models
TL;DR: In this article, a general hierarchical Bayesian framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, which they termed semiparametric hierarchical empirical likelihood (SHEL) models, is introduced.
References
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Journal ArticleDOI
Empirical likelihood ratio confidence intervals for a single functional
TL;DR: In this article, the empirical distribution function based on a sample is used to define a likelihood ratio function for distributions, which can be used to construct confidence intervals for the sample mean, for a class of M-estimates that includes quantiles, and for differentiable statistical functionals.
Journal ArticleDOI
Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators
Whitney K. Newey,Richard Smith +1 more
TL;DR: In this paper, bias corrected generalized empirical likelihood (GEL) and generalized generalized method of moments (GMM) estimators have been compared and it is shown that GEL has no asymptotic bias due to correlation of the moment functions with their Jacobian.
Journal ArticleDOI
Bayesian empirical likelihood
TL;DR: In this paper, empirical likelihood tests have many of the same asymptotic properties as those derived from parametric likelihoods, which leads naturally to the possibility of using empirical likelihood as the basis for Bayesian inference.
Journal ArticleDOI
Bayesian exponentially tilted empirical likelihood
TL;DR: In this paper, a non-parametric Bayesian procedure for moment condition models is proposed, where the probability weights are obtained via exponential tilting and the prior preference is given to distributions having a small support and among those sharing the same support.
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