Add to ORKGThe paper compares the performance of some widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained Bayes estimator by means of a new measurement under squared error loss function for the typical normal-normal situation. The proposed measurement is a weighted sum of the precisions of first and second moments. As a result, one can gets the criterion according to the size of prior variance against the population variance
AbstractThis paper examines the performance of several biased, Stein-like and empirical Bayes estima...
AbstractThis paper derives and evaluates an algorithm for estimating normal covariances. A particula...
In this thesis, we propose a median-loss-based procedure for inference. The optimal estimators under...
One of the oldest problems in statistical area is to make inference on a common mean of several diff...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesi...
Constrained Bayesian estimates overcome the over shrinkness toward the mean which usual Bayes and em...
In Bayesian approach of statistical analyses we incorporate the prior information about the paramete...
• This paper is concerned with the estimation under squared-error loss of a normal mean θ based on X...
We investigate the empirical Bayes estimation problem of multivariate regression coeffi-cients under...
In a Rayleigh distribution, we interesting of the estimation of the parameter and some feature of re...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Comparisons of estimates between Bayes and frequentist methods are inter-esting and challenging topi...
Key Words: binomial parameter n; Bayes estimators; admissibility; Blyth’s method; squared error loss...
Although the idea of Bayesian inference dates back to the late 18th century, its use by statistician...
AbstractThis paper examines the performance of several biased, Stein-like and empirical Bayes estima...
AbstractThis paper derives and evaluates an algorithm for estimating normal covariances. A particula...
In this thesis, we propose a median-loss-based procedure for inference. The optimal estimators under...
One of the oldest problems in statistical area is to make inference on a common mean of several diff...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesi...
Constrained Bayesian estimates overcome the over shrinkness toward the mean which usual Bayes and em...
In Bayesian approach of statistical analyses we incorporate the prior information about the paramete...
• This paper is concerned with the estimation under squared-error loss of a normal mean θ based on X...
We investigate the empirical Bayes estimation problem of multivariate regression coeffi-cients under...
In a Rayleigh distribution, we interesting of the estimation of the parameter and some feature of re...
Squared error loss remains the most commonly used loss function for constructing a Bayes estimator o...
Comparisons of estimates between Bayes and frequentist methods are inter-esting and challenging topi...
Key Words: binomial parameter n; Bayes estimators; admissibility; Blyth’s method; squared error loss...
Although the idea of Bayesian inference dates back to the late 18th century, its use by statistician...
AbstractThis paper examines the performance of several biased, Stein-like and empirical Bayes estima...
AbstractThis paper derives and evaluates an algorithm for estimating normal covariances. A particula...
In this thesis, we propose a median-loss-based procedure for inference. The optimal estimators under...