In 1981 Rubin introduced the Bayesian bootstrap and argued that it was the natural Bayesian analogue to the usual bootstrap. We show here that when estimating a population quantile in a nonparametric problem it yields estimators that are often preferred to the natural naive estimators based on the order statistic
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting t...
Rubin's method (Rubin 1981) is applied to construct Bayesian bootstrap confidence intervals for the ...
Rubin's method (Rubin 1981) is applied to construct Bayesian bootstrap confidence intervals for the ...
[[abstract]]Quantile information is useful in business and engineering applications, but the exact s...
Abstract The parametric bootstrap can be used for the efficient computation of Bayes posterior distr...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
Sample quantiles are consistent estimators for the true quantile and satisfy central limit theorems ...
Use of Bayesian modelling and analysis has become commonplace in many disciplines (finance, genetics...
Quantile regression deals with the problem of computing robust estimators when the conditional mean ...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting t...
Rubin's method (Rubin 1981) is applied to construct Bayesian bootstrap confidence intervals for the ...
Rubin's method (Rubin 1981) is applied to construct Bayesian bootstrap confidence intervals for the ...
[[abstract]]Quantile information is useful in business and engineering applications, but the exact s...
Abstract The parametric bootstrap can be used for the efficient computation of Bayes posterior distr...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
Sample quantiles are consistent estimators for the true quantile and satisfy central limit theorems ...
Use of Bayesian modelling and analysis has become commonplace in many disciplines (finance, genetics...
Quantile regression deals with the problem of computing robust estimators when the conditional mean ...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...