• Title/Summary/Keyword: empirical Bayes

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A Comparative Study for Several Bayesian Estimators Under Balanced Loss Function

  • Kim, Yeong-Hwa
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.291-300
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    • 2006
  • In this research, the performance of widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained empirical Bayes estimator are compared by means of a measurement under balanced 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.

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On the Performance of Empiricla Bayes Simultaneous Interval Estimates for All Pairwise Comparisons

  • Kim, Woo-Chul;Han, Kyung-Soo
    • Journal of the Korean Statistical Society
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    • v.24 no.1
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    • pp.161-181
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    • 1995
  • The goal of this article is to study the performances of various empirical Bayes simultaneous interval estimates for all pairwise comparisons. The considered empirical Bayes interval estimaters are those based on unbiased estimate, a hierarchical Bayes estimate and a constrained hierarchical Bayes estimate. Simulation results for small sample cases are given and an illustrative example is also provided.

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Robust Bayes and Empirical Bayes Analysis in Finite Population Sampling with Auxiliary Information

  • Kim, Dal-Ho
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.331-348
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    • 1998
  • In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

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A Comparative Study for Several Bayesian Estimators Under Squared Error Loss Function

  • Kim, Yeong-Hwa
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.371-382
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    • 2005
  • The 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.

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Optimal Convergence Rate of Empirical Bayes Tests for Uniform Distributions

  • Liang, Ta-Chen
    • Journal of the Korean Statistical Society
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    • v.31 no.1
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    • pp.33-43
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    • 2002
  • The empirical Bayes linear loss two-action problem is studied. An empirical Bayes test $\delta$$_{n}$ $^{*}$ is proposed. It is shown that $\delta$$_{n}$ $^{*}$ is asymptotically optimal in the sense that its regret converges to zero at a rate $n^{-1}$ over a class of priors and the rate $n^{-1}$ is the optimal rate of convergence of empirical Bayes tests.sts.

Empirical Bayes Confidence Intervals of the Burr Type XII Failure Model

  • Choi, Dal-Woo
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.155-162
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    • 1999
  • This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

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Bayes and Empirical Bayes Estimation of the Scale Parameter of the Gamma Distribution under Balanced Loss Functions

  • Rezaeian, R.;Asgharzadeh, A.
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.71-80
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    • 2007
  • The present paper investigates estimation of a scale parameter of a gamma distribution using a loss function that reflects both goodness of fit and precision of estimation. The Bayes and empirical Bayes estimators rotative to balanced loss functions (BLFs) are derived and optimality of some estimators are studied.

Empirical Bayes Nonparametric Estimation with Beta Processes Based on Censored Observations

  • Hong, Jee-Chang;Kim, Yongdai;Inha Jung
    • Journal of the Korean Statistical Society
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    • v.30 no.3
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    • pp.481-498
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    • 2001
  • Empirical Bayes procedure of nonparametric estiamtion of cumulative hazard rates based on censored data is considered using the beta process priors of Hjort(1990). Beta process priors with unknown parameters are used for cumulative hazard rates. Empirical Bayes estimators are suggested and asymptotic optimality is proved. Our result generalizes that of Susarla and Van Ryzin(1978) in the sensor that (i) the cumulative hazard rate induced by a Dirichlet process is a beta process, (ii) our empirical Bayes estimator does not depend on the censoring distribution while that of Susarla and Van Ryzin(1978) does, (iii) a class of estimators of the hyperprameters is suggested in the prior distribution which is assumed known in advance in Susarla and Van Ryzin(1978). This extension makes the proposed empirical Bayes procedure more applicable to real dta sets.

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Robust Bayes and Empirical Bayes Analysis in Finite Population Sampling

  • Dal Ho Kim
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.63-73
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    • 1995
  • We consider some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean. The proposed estimators are compared with the sample mean and subjective Bayes estimators in terms of "posterior robustness" and "procedure robustness".re robustness".uot;.

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