• 제목/요약/키워드: Empirical Bayes

검색결과 106건 처리시간 0.025초

Nonparametric empirical bayes estimation of a distribution function with respect to dirichlet process prior in case of the non-identical components (분포함수의 추정및 응용에 관한연구(Dirichlet Process에 의한 비모수 결정이론을 중심으로))

  • 정인하
    • The Korean Journal of Applied Statistics
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    • 제6권1호
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    • pp.173-181
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    • 1993
  • Nonparametric empirical Bayes estimation of a distribution function with respect to dirichlet process prior is considered when sample sizes are varying from component to component. Zehnwirth's estimate of $\alpha$(R) is modified to be used in our empirical Bayes problem with non-identical components.

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EMPIRICAL BAYES ESTIMATION OF THE TRUNCATION PARAMETER WITH ASYMMETRIC LOSS FUNCTION USING NA SAMPLES

  • Shi, Yimin;Shi, Xiaolin;Gao, Shesheng
    • Journal of applied mathematics & informatics
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    • 제14권1_2호
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    • pp.305-317
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    • 2004
  • We construct the empirical Bayes (EB)estimation of the parameter in two-side truncated distribution families with asymmetric Linex loss using negatively associated (NA) samples. The asymptotical optimality and convergence rate of the EB estimation is obtained. We will show that the convergence rate can be arbitrarily close to $O(n^{-q}),\;q\;=\;{\lambda}s(\delta\;-\;2)/\delta(s\;+\;2)$.

Empirical Bayes Estimation of the Binomial and Normal Parameters

  • Hong, Jee-Chang;Inha Jung
    • Communications for Statistical Applications and Methods
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    • 제8권1호
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    • pp.87-96
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    • 2001
  • We consider the empirical Bayes estimation problems with the binomial and normal components when the prior distributions are unknown but are assumed to be in certain families. There may be the families of all distributions on the parameter space or subfamilies such as the parametric families of conjugate priors. We treat both cases and establish the asymptotic optimality for the corresponding decision procedures.

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Empirical Bayes Inferences in the Burr Distribution by the Bootstrap Methods

  • Cho, Kil-Ho;Cho, Jang-Sik;Jeong, Seong-Hwa;Shin, Jae-Seock
    • Journal of the Korean Data and Information Science Society
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    • 제15권3호
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    • pp.625-632
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    • 2004
  • We consider the empirical Bayes confidence intervals that attain a specified level of EB coverage for the scale parameter in the Burr distribution under type II censoring data. Also, we compare the coverage probabilities and the expected confidence interval lengths for these confidence intervals through simulation study.

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An Empiricla Bayes Estimation of Multivariate nNormal Mean Vector

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제15권2호
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    • pp.97-106
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    • 1986
  • Assume that $X_1, X_2, \cdots, X_N$ are iid p-dimensional normal random vectors ($p \geq 3$) with unknown covariance matrix. The problem of estimating multivariate normal mean vector in an empirical Bayes situation is considered. Empirical Bayes estimators, obtained by Bayes treatmetn of the covariance matrix, are presented. It is shown that the estimators are minimax, each of which domainates teh maximum likelihood estimator (MLE), when the loss is nonsingular quadratic loss. We also derive approximate credibility region for the mean vector that takes advantage of the fact that the MLE is not the best estimator.

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A Study on the Posterior Density under the Bayes-empirical Bayes Models

  • Sohn, Joong-K.Sohn;Kim, Heon-Joo-Kim
    • Communications for Statistical Applications and Methods
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    • 제3권3호
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    • pp.215-223
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    • 1996
  • By using Tukey's generalized lambda distribution, appoximate posterior density is derived under the Bayes-empirical Bayes model. The sensitivity of posterior distribution to the hyperprior distribution is examined by using Tukey's generalized lambda distriburion which approximate many well-knmown distributions. Based upon Monte Varlo simulation studies it can be said that posterior distribution is sensitive to the cariance of the prior distribution and to the symmetry of the hyperprior distribution. Also posterior distribution is approximately obtained by using the following methods : Lindley method, Laplace method and Gibbs sampler method.

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Bayesian Estimation of the Normal Means under Model Perturbation

  • Kim, Dal-Ho;Han, Seung-Cheol
    • Journal of the Korean Data and Information Science Society
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    • 제17권3호
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    • pp.1009-1019
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    • 2006
  • In this paper, we consider the simultaneous estimation problem for the normal means. We set up the model structure using the several different distributions of the errors for observing their effects of model perturbation for the error terms in obtaining the empirical Bayes and hierarchical Bayes estimators. We compare the performance of those estimators under model perturbation based on a simulation study.

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Standard Error of Empirical Bayes Estimate in NONMEM$^{(R)}$ VI

  • Kang, Dong-Woo;Bae, Kyun-Seop;Houk, Brett E.;Savic, Radojka M.;Karlsson, Mats O.
    • The Korean Journal of Physiology and Pharmacology
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    • 제16권2호
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    • pp.97-106
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    • 2012
  • The pharmacokinetics/pharmacodynamics analysis software NONMEM$^{(R)}$ output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM$^{(R)}$ VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.

Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function

  • Kim, Eunyoung;Kim, Dal Ho
    • Communications for Statistical Applications and Methods
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    • 제21권3호
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    • pp.261-274
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    • 2014
  • In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.